Stool Microbiome Analysis
Kristen James
11/5/2021
About
Project: FL100
About: Preproccess the data by determining the best prevalence thresholds to use. Run “standard” microbiome analysis focusing on plasma TMAO. This includes alpha and beta diversity, ANCOM differential abundance, and DESeq2 differential abundance analyses.
Inputs: Inputs include:
- TMAO phyloseq object. Recall, the data from Dr. Kable was filtered to taxa with 2% abundance, which removes technical noise. Therefore, the remaining data is believable. Further filtering we do will be driven by our interest in more prevalent taxa. Furthermore, we should not recalculate the alpha diversity statistics after our filtering because that would incorrectly describe the microbiome environment.
- Master phenotype data
Outputs: Outputs include:
- Plots per section [expand as analysis goes final]
- Tables [expand as analysis goes final]
Sources:
Set Up
library(DESeq2)
library(phyloseq)
library(ggplot2)
library(dplyr)
library(vegan)load("../../data/processed/microbiome/phyloseq_objects/PSOtmao_211108.RData") #PSOtmao# Add age_cat
sample_data(PSOtmao)$age_cat <- ifelse(sample_data(PSOtmao)$age < 34, 1,
ifelse(sample_data(PSOtmao)$age >= 34 & sample_data(PSOtmao)$age < 50, 2, 3))
sample_data(PSOtmao)$age_cat <- as.factor(sample_data(PSOtmao)$age_cat)
# Add bmi_cat
sample_data(PSOtmao)$bmi_cat <- ifelse(sample_data(PSOtmao)$bmi_final.x < 25, 1,
ifelse(sample_data(PSOtmao)$bmi_final.x >= 25 & sample_data(PSOtmao)$bmi_final.x < 30, 2, 3))
sample_data(PSOtmao)$bmi_cat <- as.factor(sample_data(PSOtmao)$bmi_cat)
# Make sure "below" is the reference factor
str(sample_data(PSOtmao)$tmao_mdn)## Factor w/ 2 levels "above","below": 2 2 1 2 1 2 2 2 2 1 ...sample_data(PSOtmao)$tmao_mdn <- relevel(sample_data(PSOtmao)$tmao_mdn, ref = "below")
# Add TMAO quantiles
tmao_quantile <- quantile(sample_data(PSOtmao)$tmao)
sample_data(PSOtmao)$tmao_quantile <- ifelse(sample_data(PSOtmao)$tmao <= tmao_quantile[2], "Quantile1",
ifelse(sample_data(PSOtmao)$tmao > tmao_quantile[2] & sample_data(PSOtmao)$tmao <= tmao_quantile[3], "Quantile2",
ifelse(sample_data(PSOtmao)$tmao > tmao_quantile[3] & sample_data(PSOtmao)$tmao <= tmao_quantile[4], "Quantile3", "Quantile4")))
# Factor it
sample_data(PSOtmao)$tmao_quantile = factor(sample_data(PSOtmao)$tmao_quantile)
# Add TMAO tertiles
tmao_tertile <- quantile(sample_data(PSOtmao)$tmao, c(0:3/3))
sample_data(PSOtmao)$tmao_tertile <- ifelse(sample_data(PSOtmao)$tmao <= tmao_tertile[2], "Tertile1",
ifelse(sample_data(PSOtmao)$tmao > tmao_tertile[2] & sample_data(PSOtmao)$tmao <= tmao_tertile[3], "Tertile2", "Tertile3"))
# Factor it
sample_data(PSOtmao)$tmao_tertile = factor(sample_data(PSOtmao)$tmao_tertile)
# Lookss good
table(sample_data(PSOtmao)$tmao_tertile)##
## Tertile1 Tertile2 Tertile3
## 119 118 118Preproccess
Table of read counts at the Phylum level. We will filter low abundant Phyla. We will also require that taxa are present in at least 10% of samples (at least 36 samples). We will then agglomerate at the Genus level.
print("The number of different bacteria at each phyla:")## [1] "The number of different bacteria at each phyla:"table(tax_table(PSOtmao)[, "Phylum"])##
## p__Actinobacteria p__Bacteroidetes p__Cyanobacteria p__Elusimicrobia
## 45 575 8 1
## p__Firmicutes p__Fusobacteria p__Proteobacteria p__Spirochaetes
## 2042 4 44 1
## p__Synergistetes p__Tenericutes p__Verrucomicrobia
## 1 60 5# Prevalence - how many samples the taxa is observed in
prevdf = apply(X = otu_table(PSOtmao),
MARGIN = ifelse(taxa_are_rows(PSOtmao), yes = 1, no = 2),
FUN = function(x){sum(x > 0)})
prevdf = data.frame(Prevalence = prevdf,
TotalAbundance = taxa_sums(PSOtmao), # the total number of occurrences (even if only observed 50 times in 1 sample, the number would be 50)
tax_table(PSOtmao))
print("Table showing prevalence, total abundance, and taxonomy:")## [1] "Table showing prevalence, total abundance, and taxonomy:"prevdf[1:20,]## Prevalence TotalAbundance Kingdom
## 77a72e43b8d0143ee2d29c79be1b00da 25 7590 k__Bacteria
## 909ae41a723a8fd891fe419d479eedee 1 67 k__Bacteria
## ede1221982486d3a7b845b3d9b52d143 3 612 k__Bacteria
## 20f39b86c19b9d76d340da961091b85c 2 127 k__Bacteria
## a735c8388a8121dd152ff23c4542b704 1 153 k__Bacteria
## 22138725bb36874fd5109e9a0070f2f8 2 93 k__Bacteria
## 8bf97afdd2f9c12017be818206946c22 1 48 k__Bacteria
## 88efe73ef81626a8a150533dea375e82 1 151 k__Bacteria
## 54fe5a27808d4d09c806b4c562a27601 2 59 k__Bacteria
## 2b0390d3c06b1bbe94ec48f3b036a500 1 52 k__Bacteria
## aed8608657afe922ec14430de7b48f3e 1 198 k__Bacteria
## 89d1dade4f693199e3aed179f37508d1 1 87 k__Bacteria
## 6e6f0f914f6175cd071197f97b9672c4 43 558 k__Bacteria
## bf1343d2ee6d94673c0907bc566ea9d9 76 858 k__Bacteria
## f1d7ac8c18c1d8144a00e4d785c86e4e 47 529 k__Bacteria
## 484ead003b64fe20c520597fa7797d23 34 1128 k__Bacteria
## 344bc48fbf2d6572954c51cdf104061b 118 2177 k__Bacteria
## 3520cbb42eb18c7d0889d4acd1fb17c1 1 41 k__Bacteria
## 5adf9526730ed772ee3ef6cd39caeee6 2 81 k__Bacteria
## f28bcb82096cccf4d2334f12b76c3d8e 126 4355 k__Bacteria
## Phylum Class
## 77a72e43b8d0143ee2d29c79be1b00da p__Bacteroidetes c__Bacteroidia
## 909ae41a723a8fd891fe419d479eedee p__Bacteroidetes c__Bacteroidia
## ede1221982486d3a7b845b3d9b52d143 p__Bacteroidetes c__Bacteroidia
## 20f39b86c19b9d76d340da961091b85c p__Bacteroidetes c__Bacteroidia
## a735c8388a8121dd152ff23c4542b704 p__Bacteroidetes c__Bacteroidia
## 22138725bb36874fd5109e9a0070f2f8 p__Bacteroidetes c__Bacteroidia
## 8bf97afdd2f9c12017be818206946c22 p__Bacteroidetes c__Bacteroidia
## 88efe73ef81626a8a150533dea375e82 p__Bacteroidetes c__Bacteroidia
## 54fe5a27808d4d09c806b4c562a27601 p__Bacteroidetes c__Bacteroidia
## 2b0390d3c06b1bbe94ec48f3b036a500 p__Bacteroidetes c__Bacteroidia
## aed8608657afe922ec14430de7b48f3e p__Bacteroidetes c__Bacteroidia
## 89d1dade4f693199e3aed179f37508d1 p__Bacteroidetes c__Bacteroidia
## 6e6f0f914f6175cd071197f97b9672c4 p__Bacteroidetes c__Bacteroidia
## bf1343d2ee6d94673c0907bc566ea9d9 p__Bacteroidetes c__Bacteroidia
## f1d7ac8c18c1d8144a00e4d785c86e4e p__Bacteroidetes c__Bacteroidia
## 484ead003b64fe20c520597fa7797d23 p__Bacteroidetes c__Bacteroidia
## 344bc48fbf2d6572954c51cdf104061b p__Bacteroidetes c__Bacteroidia
## 3520cbb42eb18c7d0889d4acd1fb17c1 p__Bacteroidetes c__Bacteroidia
## 5adf9526730ed772ee3ef6cd39caeee6 p__Bacteroidetes c__Bacteroidia
## f28bcb82096cccf4d2334f12b76c3d8e p__Bacteroidetes c__Bacteroidia
## Order Family
## 77a72e43b8d0143ee2d29c79be1b00da o__Bacteroidales f__Prevotellaceae
## 909ae41a723a8fd891fe419d479eedee o__Bacteroidales f__Prevotellaceae
## ede1221982486d3a7b845b3d9b52d143 o__Bacteroidales f__Prevotellaceae
## 20f39b86c19b9d76d340da961091b85c o__Bacteroidales f__
## a735c8388a8121dd152ff23c4542b704 o__Bacteroidales f__
## 22138725bb36874fd5109e9a0070f2f8 o__Bacteroidales f__
## 8bf97afdd2f9c12017be818206946c22 o__Bacteroidales f__
## 88efe73ef81626a8a150533dea375e82 o__Bacteroidales f__
## 54fe5a27808d4d09c806b4c562a27601 o__Bacteroidales f__
## 2b0390d3c06b1bbe94ec48f3b036a500 o__Bacteroidales f__
## aed8608657afe922ec14430de7b48f3e o__Bacteroidales f__
## 89d1dade4f693199e3aed179f37508d1 o__Bacteroidales f__Rikenellaceae
## 6e6f0f914f6175cd071197f97b9672c4 o__Bacteroidales f__Rikenellaceae
## bf1343d2ee6d94673c0907bc566ea9d9 o__Bacteroidales f__Rikenellaceae
## f1d7ac8c18c1d8144a00e4d785c86e4e o__Bacteroidales f__Rikenellaceae
## 484ead003b64fe20c520597fa7797d23 o__Bacteroidales f__Rikenellaceae
## 344bc48fbf2d6572954c51cdf104061b o__Bacteroidales f__Rikenellaceae
## 3520cbb42eb18c7d0889d4acd1fb17c1 o__Bacteroidales f__Rikenellaceae
## 5adf9526730ed772ee3ef6cd39caeee6 o__Bacteroidales f__Rikenellaceae
## f28bcb82096cccf4d2334f12b76c3d8e o__Bacteroidales f__Rikenellaceae
## Genus Species
## 77a72e43b8d0143ee2d29c79be1b00da g__ s__
## 909ae41a723a8fd891fe419d479eedee g__ s__
## ede1221982486d3a7b845b3d9b52d143 g__ s__
## 20f39b86c19b9d76d340da961091b85c g__ s__
## a735c8388a8121dd152ff23c4542b704 g__ s__
## 22138725bb36874fd5109e9a0070f2f8 g__ s__
## 8bf97afdd2f9c12017be818206946c22 g__ s__
## 88efe73ef81626a8a150533dea375e82 g__ s__
## 54fe5a27808d4d09c806b4c562a27601 g__ s__
## 2b0390d3c06b1bbe94ec48f3b036a500 g__ s__
## aed8608657afe922ec14430de7b48f3e g__ s__
## 89d1dade4f693199e3aed179f37508d1 g__Rikenella s__
## 6e6f0f914f6175cd071197f97b9672c4 g__ s__
## bf1343d2ee6d94673c0907bc566ea9d9 g__ s__
## f1d7ac8c18c1d8144a00e4d785c86e4e g__Alistipes s__indistinctus
## 484ead003b64fe20c520597fa7797d23 g__ s__
## 344bc48fbf2d6572954c51cdf104061b g__ s__
## 3520cbb42eb18c7d0889d4acd1fb17c1 g__ s__
## 5adf9526730ed772ee3ef6cd39caeee6 g__Alistipes s__finegoldii
## f28bcb82096cccf4d2334f12b76c3d8e <NA> <NA># Make table with mean prevalence, total prevalence
print("Table showing mean prevalencce and total prevalence per Phylum:")## [1] "Table showing mean prevalencce and total prevalence per Phylum:"plyr::ddply(prevdf, "Phylum", function(df_mb){cbind("Mean" = mean(df_mb$Prevalence),"Sum" = sum(df_mb$Prevalence))})## Phylum Mean Sum
## 1 p__Actinobacteria 32.733333 1473
## 2 p__Bacteroidetes 12.624348 7259
## 3 p__Cyanobacteria 5.125000 41
## 4 p__Elusimicrobia 2.000000 2
## 5 p__Firmicutes 20.987757 42857
## 6 p__Fusobacteria 3.250000 13
## 7 p__Proteobacteria 21.159091 931
## 8 p__Spirochaetes 1.000000 1
## 9 p__Synergistetes 23.000000 23
## 10 p__Tenericutes 5.966667 358
## 11 p__Verrucomicrobia 35.000000 175From the phylum table, we can see that p__Elusimicrobia, p__Spirochaetes, and p__Synergistetes have the same mean and sum, meaning they are only present in 1 sample. We are interested and confident in the more abundant taxa so manually filter them out. Per Dr. Kable’s expert advice, she already filtered to 2% prevalence to remove technical error. We will increase the filtering to 5%, a common threshold in the literature.
# Define prevalence threshold as 5% of total samples
prevalenceThreshold <- 0.05 * nsamples(PSOtmao)
cat("The 10% prevalence threshold is", prevalenceThreshold, "samples.", "\n")## The 10% prevalence threshold is 17.75 samples.# Execute prevalence filter, using `prune_taxa()` function
keepTaxa = rownames(prevdf)[(prevdf$Prevalence >= prevalenceThreshold)]
PSOtmao1 = prune_taxa(keepTaxa, PSOtmao)
print("The number of different bacteria at each phyla:")## [1] "The number of different bacteria at each phyla:"table(tax_table(PSOtmao1)[, "Phylum"])##
## p__Actinobacteria p__Bacteroidetes p__Firmicutes p__Proteobacteria
## 15 67 407 11
## p__Synergistetes p__Tenericutes p__Verrucomicrobia
## 1 4 3# Look at phylum level distributions
ggplot(prevdf, aes(TotalAbundance, Prevalence / nsamples(PSOtmao1),color=Phylum)) +
geom_hline(yintercept = 0.05, alpha = 0.5, linetype = 2) + # Include a guess for parameter
geom_point(size = 2, alpha = 0.7) +
scale_x_log10() +
xlab("Total Abundance") +
ylab("Prevalence [Frac. Samples]") +
facet_wrap(~Phylum) +
theme(legend.position="none")
# Agglomerate taxa
PSOtmao1G = tax_glom(PSOtmao1, "Genus", NArm = TRUE)Great. Do your due diligence and inspect each phyloseq object to see how the filtering and glomming steps changed the number of taxonomy.
# Inspect the effect of each filtering step on the phyloseq object
PSOtmao## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 2786 taxa and 355 samples ]
## sample_data() Sample Data: [ 355 samples by 64 sample variables ]
## tax_table() Taxonomy Table: [ 2786 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 2786 tips and 2730 internal nodes ]PSOtmao1## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 508 taxa and 355 samples ]
## sample_data() Sample Data: [ 355 samples by 64 sample variables ]
## tax_table() Taxonomy Table: [ 508 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 508 tips and 502 internal nodes ]PSOtmao1G## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 67 taxa and 355 samples ]
## sample_data() Sample Data: [ 355 samples by 64 sample variables ]
## tax_table() Taxonomy Table: [ 67 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 67 tips and 66 internal nodes ]Look at Genus level distributions of the bugs.
# Prevalence - how many samples the taxa is observed in
prevdf = apply(X = otu_table(PSOtmao1G),
MARGIN = ifelse(taxa_are_rows(PSOtmao1G), yes = 1, no = 2),
FUN = function(x){sum(x > 0)})
prevdf = data.frame(Prevalence = prevdf,
TotalAbundance = taxa_sums(PSOtmao1G), # the total number of occurrences (even if only observed 50 times in 1 sample, the number would be 50)
tax_table(PSOtmao1G))
print("Table showing prevalence, total abundance, and taxonomy:")## [1] "Table showing prevalence, total abundance, and taxonomy:"prevdf[1:10,]## Prevalence TotalAbundance Kingdom
## 77a72e43b8d0143ee2d29c79be1b00da 25 7590 k__Bacteria
## f1d7ac8c18c1d8144a00e4d785c86e4e 47 529 k__Bacteria
## 7958dd98cfbbdc096240cd1dc4531423 236 10107 k__Bacteria
## 05404c9fdf9f3f334eb618bac3f434f6 228 4099 k__Bacteria
## 06105df60508c2ed24a54f1b8ed64e49 326 47062 k__Bacteria
## 7ce12788fd2f8479636e23aae67e09c8 174 12619 k__Bacteria
## 3f6503536588cb53c493d4c42e94a338 20 2818 k__Bacteria
## bb1b75f41ff9c9db1d1de41e8388eb52 355 640634 k__Bacteria
## 1efe70e365249c0d2fc28580b6ba0529 56 4097 k__Bacteria
## 098c3bbd8234f4ac198297ac0bde957d 178 499064 k__Bacteria
## Phylum Class
## 77a72e43b8d0143ee2d29c79be1b00da p__Bacteroidetes c__Bacteroidia
## f1d7ac8c18c1d8144a00e4d785c86e4e p__Bacteroidetes c__Bacteroidia
## 7958dd98cfbbdc096240cd1dc4531423 p__Bacteroidetes c__Bacteroidia
## 05404c9fdf9f3f334eb618bac3f434f6 p__Bacteroidetes c__Bacteroidia
## 06105df60508c2ed24a54f1b8ed64e49 p__Bacteroidetes c__Bacteroidia
## 7ce12788fd2f8479636e23aae67e09c8 p__Bacteroidetes c__Bacteroidia
## 3f6503536588cb53c493d4c42e94a338 p__Bacteroidetes c__Bacteroidia
## bb1b75f41ff9c9db1d1de41e8388eb52 p__Bacteroidetes c__Bacteroidia
## 1efe70e365249c0d2fc28580b6ba0529 p__Bacteroidetes c__Bacteroidia
## 098c3bbd8234f4ac198297ac0bde957d p__Bacteroidetes c__Bacteroidia
## Order Family
## 77a72e43b8d0143ee2d29c79be1b00da o__Bacteroidales f__Prevotellaceae
## f1d7ac8c18c1d8144a00e4d785c86e4e o__Bacteroidales f__Rikenellaceae
## 7958dd98cfbbdc096240cd1dc4531423 o__Bacteroidales f__Rikenellaceae
## 05404c9fdf9f3f334eb618bac3f434f6 o__Bacteroidales f__[Odoribacteraceae]
## 06105df60508c2ed24a54f1b8ed64e49 o__Bacteroidales f__Porphyromonadaceae
## 7ce12788fd2f8479636e23aae67e09c8 o__Bacteroidales f__[Barnesiellaceae]
## 3f6503536588cb53c493d4c42e94a338 o__Bacteroidales f__S24-7
## bb1b75f41ff9c9db1d1de41e8388eb52 o__Bacteroidales f__Bacteroidaceae
## 1efe70e365249c0d2fc28580b6ba0529 o__Bacteroidales f__[Paraprevotellaceae]
## 098c3bbd8234f4ac198297ac0bde957d o__Bacteroidales f__Prevotellaceae
## Genus Species
## 77a72e43b8d0143ee2d29c79be1b00da g__ <NA>
## f1d7ac8c18c1d8144a00e4d785c86e4e g__Alistipes <NA>
## 7958dd98cfbbdc096240cd1dc4531423 g__ <NA>
## 05404c9fdf9f3f334eb618bac3f434f6 g__Odoribacter <NA>
## 06105df60508c2ed24a54f1b8ed64e49 g__Parabacteroides <NA>
## 7ce12788fd2f8479636e23aae67e09c8 g__ <NA>
## 3f6503536588cb53c493d4c42e94a338 g__ <NA>
## bb1b75f41ff9c9db1d1de41e8388eb52 g__Bacteroides <NA>
## 1efe70e365249c0d2fc28580b6ba0529 g__Paraprevotella <NA>
## 098c3bbd8234f4ac198297ac0bde957d g__Prevotella <NA># Family
ggplot(prevdf, aes(TotalAbundance, Prevalence / nsamples(PSOtmao1G),color=Family)) +
geom_hline(yintercept = 0.05, alpha = 0.5, linetype = 2) + # Include a guess for parameter
geom_point(size = 1, alpha = 0.7) +
scale_x_log10() +
xlab("Total Abundance") +
ylab("Prevalence [Frac. Samples]") +
facet_wrap(~Family) +
theme(legend.position="none")
# Genus - have to blow it up to make it useful
ggplot(prevdf, aes(TotalAbundance, Prevalence / nsamples(PSOtmao1G),color=Genus)) +
geom_hline(yintercept = 0.05, alpha = 0.5, linetype = 2) + # Include a guess for parameter
geom_point(size = 1, alpha = 0.7) +
scale_x_log10() +
xlab("Total Abundance") +
ylab("Prevalence [Frac. Samples]") +
facet_wrap(~Genus) +
theme(legend.position="none")
Look for Outliers Using PCoA
We want to identify outlier taxa that might drive the statistical analyses. One way to do this is to look for outliers in the PCoA plot.
PCoA_BC <- ordinate(PSOtmao1G, "PCoA", "bray")
# BC
# taxa is similar to species
taxa_plot <- plot_ordination(PSOtmao1G, PCoA_BC, type = "taxa")
taxa_plot
# It looks like there is an outlier in the top left corner of the plot.
# Identify which taxa that bacteria is from and remove it.
tp_data <- taxa_plot$data
plot(tp_data$Axis.2) # yes an outlier!
# Who is it?
outlier <- tp_data[tp_data$Axis.2 == max(tp_data$Axis.2), ]
cat("Which genus is the outlier?", outlier$Genus, "\n")## Which genus is the outlier? g__PrevotellaSuper interesting! The “outlier” is prevotella, which is interesting in the microbiome community as a marker of individuals who consume/live/have non-industrialized diets/environments/guts. We won’t remove this bug afterall.
Save
save(PSOtmao1, file = "../../data/processed/microbiome/phyloseq_objects/PSOtmao1_211118.RData")
save(PSOtmao1G, file = "../../data/processed/microbiome/phyloseq_objects/PSOtmao1G_211118.RData")Phylogenetic Abundance plots
Family Plot of TMAO Quantile
# Agglomerate taxa
PSOtmao1F = tax_glom(PSOtmao1, "Family", NArm = TRUE)
PSOtmao1F.top10 <- microbiome::aggregate_top_taxa(PSOtmao1F, "Family", top = 10)## Warning: 'microbiome::aggregate_top_taxa' is deprecated.
## Use 'aggregate_rare' instead.
## See help("Deprecated") and help("The microbiome::aggregate_top_taxa function is deprecated.-deprecated").ps1F.10.family.comp <- microbiome::transform(PSOtmao1F.top10, transform="compositional")
plot.composition.relAbun <- microbiome::plot_composition(ps1F.10.family.comp,
sample.sort = "Description",
x.label = "tmao_quantile",
group_by = "tmao_quantile",
average_by = "tmao_quantile") +
theme_minimal() +
scale_fill_brewer("Family", palette = "Paired") +
ggtitle("Relative Abundance at Family Level")
plot.composition.relAbun
Family Plot of TMAO Tertile
# Agglomerate taxa
PSOtmao1F = tax_glom(PSOtmao1, "Family", NArm = TRUE)
PSOtmao1F.top10 <- microbiome::aggregate_top_taxa(PSOtmao1F, "Family", top = 10)## Warning: 'microbiome::aggregate_top_taxa' is deprecated.
## Use 'aggregate_rare' instead.
## See help("Deprecated") and help("The microbiome::aggregate_top_taxa function is deprecated.-deprecated").ps1F.10.family.comp <- microbiome::transform(PSOtmao1F.top10, transform="compositional")
plot.composition.relAbun <- microbiome::plot_composition(ps1F.10.family.comp,
sample.sort = "Description",
x.label = "tmao_tertile",
group_by = "tmao_tertile",
average_by = "tmao_tertile") +
theme_minimal() +
scale_fill_brewer("Family", palette = "Paired") +
ggtitle("Relative Abundance at Family Level")
plot.composition.relAbun
# Save
jpeg("../../Outputs/plots/microbiome/RelAbund_Fam_TMAOTertiles_2111.jpg", width = 4, height = 4, res = 300)
plot.composition.relAbun
dev.off()## quartz_off_screen
## 2DESeq2
From the DESeq2 documentation, “Note: In order to benefit from the default settings of the package, you should put the variable of interest at the end of the formula and make sure the control level is the first level.”
From Dr. Kable’s example,
DESeq2 Set Up Test the impact of fiber cluster, accounting for covariates, using the log ratio test
dds.ratio.batcho = phyloseq_to_deseq2(ps.genus, ~ Batch + k4_cluster) This performs a likelihood ratio test, which determines if the increased likelihood of the data in the full model, where we include all terms, is more than expected if the terms excluded in the reduced model are really zero.
dds.ratio.batcho <- DESeq(dds.ratio.batcho, test=“LRT”, reduced = ~ Batch )
The p-value is based on the LRT (so the same for everything), but the contrasts give the right log2 fold change.
resultsNames(dds.ratio.batcho) res.1v2.batcho <- results(dds.ratio.batcho, contrast=c(“k4_cluster”, “2”, “1”))
Factored by TMAO Median
# Grab the sample data to easily check the structure of your covariates, age, sex, and BMI
sd <- sample_data(PSOtmao1G)
str(sd$sex)## int [1:355] 1 2 2 2 2 2 2 2 2 1 ...str(sd$bmi_final.x)## num [1:355] 20.8 23 21.6 27.5 25.9 ...# Factor sex, BMI okay
sample_data(PSOtmao1G)$sex = factor(sample_data(PSOtmao1G)$sex)
#~~~~~~~~~~~~~~~
# DESeq analysis
# TMAO median
#~~~~~~~~~~~~~~~
mb <- phyloseq_to_deseq2(PSOtmao1G, ~ sex + age + bmi_final.x + tmao_mdn)## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.mb.ratio <- DESeq(mb, test = "LRT", reduced = ~ sex + age + bmi_final.x) # remove variable of interest here, TMAO## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.## final dispersion estimates## fitting model and testingres.ratio <- results(mb.ratio)
alpha = 0.05
alpha.big = 0.5
sigtab = res.ratio[which(res.ratio$padj < alpha.big), ]
sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(PSOtmao1G)[rownames(sigtab), ], "matrix"))
sigtab$family_genus <- paste0(sigtab$Family, sigtab$Genus)
head(sigtab)## baseMean log2FoldChange lfcSE stat
## bb1b75f41ff9c9db1d1de41e8388eb52 1474.719887 -0.3703151 0.1318412 7.620744
## 803eb52cfe3d77bdf9fe14c011e425fb 3.818082 -1.1460648 0.5313296 4.075223
## 2dc2cf60102ff00cd077f2304ec84d06 172.043782 -0.4458628 0.2319727 3.840854
## c5859b7f5d14b8c145fbc3ad583c70e8 8.828642 -0.5638356 1.1081776 5.714706
## 5c4ca852b40641b3eb0ad23e69bb6583 1191.355827 -0.3336365 0.1345189 6.135718
## e4ae256bb51896c21795f743dc9ed9dd 414.885235 -0.2945594 0.1475637 3.930779
## pvalue padj Kingdom
## bb1b75f41ff9c9db1d1de41e8388eb52 0.005770069 0.2817917 k__Bacteria
## 803eb52cfe3d77bdf9fe14c011e425fb 0.043516526 0.4787440 k__Bacteria
## 2dc2cf60102ff00cd077f2304ec84d06 0.050018027 0.4787440 k__Bacteria
## c5859b7f5d14b8c145fbc3ad583c70e8 0.016823386 0.2817917 k__Bacteria
## 5c4ca852b40641b3eb0ad23e69bb6583 0.013247776 0.2817917 k__Bacteria
## e4ae256bb51896c21795f743dc9ed9dd 0.047409966 0.4787440 k__Bacteria
## Phylum Class
## bb1b75f41ff9c9db1d1de41e8388eb52 p__Bacteroidetes c__Bacteroidia
## 803eb52cfe3d77bdf9fe14c011e425fb p__Actinobacteria c__Coriobacteriia
## 2dc2cf60102ff00cd077f2304ec84d06 p__Firmicutes c__Bacilli
## c5859b7f5d14b8c145fbc3ad583c70e8 p__Firmicutes c__Erysipelotrichi
## 5c4ca852b40641b3eb0ad23e69bb6583 p__Firmicutes c__Clostridia
## e4ae256bb51896c21795f743dc9ed9dd p__Firmicutes c__Clostridia
## Order Family
## bb1b75f41ff9c9db1d1de41e8388eb52 o__Bacteroidales f__Bacteroidaceae
## 803eb52cfe3d77bdf9fe14c011e425fb o__Coriobacteriales f__Coriobacteriaceae
## 2dc2cf60102ff00cd077f2304ec84d06 o__Lactobacillales f__Streptococcaceae
## c5859b7f5d14b8c145fbc3ad583c70e8 o__Erysipelotrichales f__Erysipelotrichaceae
## 5c4ca852b40641b3eb0ad23e69bb6583 o__Clostridiales f__Lachnospiraceae
## e4ae256bb51896c21795f743dc9ed9dd o__Clostridiales f__Lachnospiraceae
## Genus Species
## bb1b75f41ff9c9db1d1de41e8388eb52 g__Bacteroides <NA>
## 803eb52cfe3d77bdf9fe14c011e425fb g__Eggerthella <NA>
## 2dc2cf60102ff00cd077f2304ec84d06 g__Streptococcus <NA>
## c5859b7f5d14b8c145fbc3ad583c70e8 g__Coprobacillus <NA>
## 5c4ca852b40641b3eb0ad23e69bb6583 g__Roseburia <NA>
## e4ae256bb51896c21795f743dc9ed9dd g__[Ruminococcus] <NA>
## family_genus
## bb1b75f41ff9c9db1d1de41e8388eb52 f__Bacteroidaceae g__Bacteroides
## 803eb52cfe3d77bdf9fe14c011e425fb f__Coriobacteriaceae g__Eggerthella
## 2dc2cf60102ff00cd077f2304ec84d06 f__Streptococcaceae g__Streptococcus
## c5859b7f5d14b8c145fbc3ad583c70e8 f__Erysipelotrichaceae g__Coprobacillus
## 5c4ca852b40641b3eb0ad23e69bb6583 f__Lachnospiraceae g__Roseburia
## e4ae256bb51896c21795f743dc9ed9dd f__Lachnospiraceae g__[Ruminococcus]No results when we look at TMAO median and control for age, sex, and BMI.
Factored by TMAO Tertile
# DESeq analysis - tmao_tertile
mb <- phyloseq_to_deseq2(PSOtmao1G, ~ sex + age + bmi_final.x + tmao_tertile)## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.mb.ratio <- DESeq(mb, test = "LRT", reduced = ~ sex + age + bmi_final.x) # remove variable of interest here, TMAO## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.## final dispersion estimates## fitting model and testingres.ratio <- results(mb.ratio, contrast = c("tmao_tertile", "Tertile1", "Tertile3")) # Look at most different tertiles
alpha = 0.05
sigtab = res.ratio[which(res.ratio$padj < alpha), ]
sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(PSOtmao1G)[rownames(sigtab), ], "matrix"))
sigtab$family_genus <- paste0(sigtab$Family, sigtab$Genus)
head(sigtab)## baseMean log2FoldChange lfcSE
## bb1b75f41ff9c9db1d1de41e8388eb52 1474.719887 0.6401572 0.1593567
## b180a2455b236c103cf0bfe620095736 147.403464 0.7597943 0.3009158
## e1a2800b24cdf9779b28dc897cddb12a 77.392479 -0.9455906 0.4988914
## 8f5ceded1cd7e86b8271d3fab24d322a 8.831774 4.0416798 2.1608051
## stat pvalue padj
## bb1b75f41ff9c9db1d1de41e8388eb52 15.92794 3.477689e-04 7.766840e-03
## b180a2455b236c103cf0bfe620095736 13.32077 1.280655e-03 2.145097e-02
## e1a2800b24cdf9779b28dc897cddb12a 2751.80443 0.000000e+00 0.000000e+00
## 8f5ceded1cd7e86b8271d3fab24d322a 53.79374 2.083711e-12 6.980431e-11
## Kingdom Phylum Class
## bb1b75f41ff9c9db1d1de41e8388eb52 k__Bacteria p__Bacteroidetes c__Bacteroidia
## b180a2455b236c103cf0bfe620095736 k__Bacteria p__Firmicutes c__Clostridia
## e1a2800b24cdf9779b28dc897cddb12a k__Bacteria p__Firmicutes c__Clostridia
## 8f5ceded1cd7e86b8271d3fab24d322a k__Bacteria p__Firmicutes c__Clostridia
## Order Family
## bb1b75f41ff9c9db1d1de41e8388eb52 o__Bacteroidales f__Bacteroidaceae
## b180a2455b236c103cf0bfe620095736 o__Clostridiales f__Peptostreptococcaceae
## e1a2800b24cdf9779b28dc897cddb12a o__Clostridiales f__Christensenellaceae
## 8f5ceded1cd7e86b8271d3fab24d322a o__Clostridiales f__Lachnospiraceae
## Genus Species
## bb1b75f41ff9c9db1d1de41e8388eb52 g__Bacteroides <NA>
## b180a2455b236c103cf0bfe620095736 g__ <NA>
## e1a2800b24cdf9779b28dc897cddb12a g__ <NA>
## 8f5ceded1cd7e86b8271d3fab24d322a g__Butyrivibrio <NA>
## family_genus
## bb1b75f41ff9c9db1d1de41e8388eb52 f__Bacteroidaceae g__Bacteroides
## b180a2455b236c103cf0bfe620095736 f__Peptostreptococcaceae g__
## e1a2800b24cdf9779b28dc897cddb12a f__Christensenellaceae g__
## 8f5ceded1cd7e86b8271d3fab24d322a f__Lachnospiraceae g__Butyrivibrio#~~~~~~~~~~~~~~~
# Plot Results
#~~~~~~~~~~~~~~~
resOTU <- rownames(sigtab)
otuData <- as.data.frame(otu_table(PSOtmao1G))
otuData_desRes <- otuData[rownames(otuData) %in% resOTU,]
#otuData_desRes <- as.data.frame(t(otuData_desRes))
# Get sample data groupings
sampleData <- as.data.frame(sample_data(PSOtmao1G))
sampleData <- subset(sampleData, select = c(tmao_mdn, tmao_quantile, tmao_tertile, tmao))
# Get tax table
taxaData <- as.data.frame(tax_table(PSOtmao1G))
taxaData_res <- taxaData[rownames(taxaData) %in% resOTU,]
taxaData_res$OrderFamilyGenus <- paste0(taxaData_res$Order, taxaData_res$Family, taxaData_res$Genus)
# Merge
ggData <- merge(taxaData_res, otuData_desRes, by = 0)
rownames(ggData) <- ggData$OrderFamilyGenus
ggData <- ggData[,-(1:9)]
ggData <- as.data.frame(t(ggData))
ggData <- merge(ggData, sampleData, by = 0)
rownames(ggData) <- ggData[,"Row.names"]
ggData <- subset(ggData, select = -c(Row.names))
# Loop Plot
nres <- length(resOTU)
for (i in 1:nres) {
print(ggplot(ggData, aes(x = tmao_tertile, y = log(ggData[,i]))) +
geom_boxplot()+
ylab(colnames(ggData)[i]))
}## Warning: Removed 336 rows containing non-finite values (stat_boxplot).
## Warning: Removed 45 rows containing non-finite values (stat_boxplot).
## Warning: Removed 144 rows containing non-finite values (stat_boxplot).
#~~~~~~~~~~~~~~~
# Plot Results Fold Change
#~~~~~~~~~~~~~~~
sigtabgen = subset(sigtab, !is.na(Genus))
# Phylum order
x = tapply(sigtabgen$log2FoldChange, sigtabgen$Phylum, function(x) max(x))
x = sort(x, TRUE)
sigtabgen$Phylum = factor(as.character(sigtabgen$Phylum), levels=names(x))
# Genus order
x = tapply(sigtabgen$log2FoldChange, sigtabgen$Genus, function(x) max(x))
x = sort(x, TRUE)
sigtabgen$Genus = factor(as.character(sigtabgen$Genus), levels=names(x))
ggplot(sigtabgen, aes(y=family_genus, x=log2FoldChange, color=Phylum)) +
geom_vline(xintercept = 0.0, color = "gray", size = 0.5) +
geom_point(size=3) +
ylab("Taxonomy") +
xlab("Log2 Fold Change") +
scale_color_brewer(palette = "Dark2") +
theme_minimal() +
theme(axis.text.x = element_text(angle = -90, hjust = 0, vjust=0.5))
Factored by TMAO Quantile
# DESeq analysis - tmao_quantile
mb <- phyloseq_to_deseq2(PSOtmao1G, ~ sex + age + bmi_final.x + tmao_quantile)## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.mb.ratio <- DESeq(mb, test = "LRT", reduced = ~ sex + age + bmi_final.x) # remove variable of interest here, TMAO## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.## final dispersion estimates## fitting model and testing## 2 rows did not converge in beta, labelled in mcols(object)$fullBetaConv. Use larger maxit argument with nbinomLRTres.ratio <- results(mb.ratio, contrast = c("tmao_quantile", "Quantile1", "Quantile4"))
alpha = 0.05
sigtab = res.ratio[which(res.ratio$padj < alpha), ]
sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(PSOtmao1G)[rownames(sigtab), ], "matrix"))
sigtab$family_genus <- paste0(sigtab$Family, sigtab$Genus)
head(sigtab)## baseMean log2FoldChange lfcSE stat
## bb1b75f41ff9c9db1d1de41e8388eb52 1474.71989 0.7647902 0.1838360 17.26361
## e1a2800b24cdf9779b28dc897cddb12a 77.39248 -1.3816904 0.5762928 2747.03024
## pvalue padj Kingdom
## bb1b75f41ff9c9db1d1de41e8388eb52 0.0006237715 0.02089635 k__Bacteria
## e1a2800b24cdf9779b28dc897cddb12a 0.0000000000 0.00000000 k__Bacteria
## Phylum Class
## bb1b75f41ff9c9db1d1de41e8388eb52 p__Bacteroidetes c__Bacteroidia
## e1a2800b24cdf9779b28dc897cddb12a p__Firmicutes c__Clostridia
## Order Family
## bb1b75f41ff9c9db1d1de41e8388eb52 o__Bacteroidales f__Bacteroidaceae
## e1a2800b24cdf9779b28dc897cddb12a o__Clostridiales f__Christensenellaceae
## Genus Species
## bb1b75f41ff9c9db1d1de41e8388eb52 g__Bacteroides <NA>
## e1a2800b24cdf9779b28dc897cddb12a g__ <NA>
## family_genus
## bb1b75f41ff9c9db1d1de41e8388eb52 f__Bacteroidaceae g__Bacteroides
## e1a2800b24cdf9779b28dc897cddb12a f__Christensenellaceae g__#~~~~~~~~~~~~~~~
# Plot Results
#~~~~~~~~~~~~~~~
resOTU <- rownames(sigtab)
otuData <- as.data.frame(otu_table(PSOtmao1G))
otuData_desRes <- otuData[rownames(otuData) %in% resOTU,]
#otuData_desRes <- as.data.frame(t(otuData_desRes))
# Get sample data groupings
sampleData <- as.data.frame(sample_data(PSOtmao1G))
sampleData <- subset(sampleData, select = c(tmao_mdn, tmao_quantile, tmao_tertile, tmao))
# Get tax table
taxaData <- as.data.frame(tax_table(PSOtmao1G))
taxaData_res <- taxaData[rownames(taxaData) %in% resOTU,]
taxaData_res$OrderFamilyGenus <- paste0(taxaData_res$Order, taxaData_res$Family, taxaData_res$Genus)
# Merge
ggData <- merge(taxaData_res, otuData_desRes, by = 0)
rownames(ggData) <- ggData$OrderFamilyGenus
ggData <- ggData[,-(1:9)]
ggData <- as.data.frame(t(ggData))
ggData <- merge(ggData, sampleData, by = 0)
rownames(ggData) <- ggData[,"Row.names"]
ggData <- subset(ggData, select = -c(Row.names))
# Loop Plot
nres <- length(resOTU)
for (i in 1:nres) {
print(ggplot(ggData, aes(x = tmao_quantile, y = log(ggData[,i]))) +
geom_boxplot()+
ylab(colnames(ggData)[i]))
}
## Warning: Removed 144 rows containing non-finite values (stat_boxplot).
#~~~~~~~~~~~~~~~
# Plot Results Fold Change
#~~~~~~~~~~~~~~~
sigtabgen = subset(sigtab, !is.na(Genus))
# Phylum order
x = tapply(sigtabgen$log2FoldChange, sigtabgen$Phylum, function(x) max(x))
x = sort(x, TRUE)
sigtabgen$Phylum = factor(as.character(sigtabgen$Phylum), levels=names(x))
# Genus order
x = tapply(sigtabgen$log2FoldChange, sigtabgen$Genus, function(x) max(x))
x = sort(x, TRUE)
sigtabgen$Genus = factor(as.character(sigtabgen$Genus), levels=names(x))
ggplot(sigtabgen, aes(y=family_genus, x=log2FoldChange, color=Phylum)) +
geom_vline(xintercept = 0.0, color = "gray", size = 0.5) +
geom_point(size=3) +
ylab("Taxonomy") +
xlab("Log2 Fold Change") +
scale_color_brewer(palette = "Dark2") +
theme_minimal() +
theme(axis.text.x = element_text(angle = -90, hjust = 0, vjust=0.5))
Alpha Diversity Analysis
We do not need to redo the alpha diversity calculations because the filtering we did was not to remove technical variation but instead to focus our analysis on the more common players (also so we can be more statistically confident using less scarce data). Note, if you did want to calculate alpha diversity, the command estimate_richness() can accomplish that for you.
Check the distributions of the alpha diversity metrics and run the multiple linear regression models with covariates.
# Load master pheno file
phen <- readRDS("../../data/processed/phenotype/Phenotypes_211108.rds")
phen <- subset(phen, select = -c(sex, age, age_cat, bmi_cat))
# Grab PSO sample data
df <- as(sample_data(PSOtmao1G), "data.frame")
# Merge
df2 <- merge(phen, df, by = 0)
row.names(df2) <- df2[,"Row.names"]
df2 <- subset(df2, select =-c(Row.names))
#~~~~~~~~~~~~~~~~
# Compute
#~~~~~~~~~~~~~~~~
# Check distribution
shapiro.test(df2$shannon) # not normal, proceed with caution##
## Shapiro-Wilk normality test
##
## data: df2$shannon
## W = 0.905, p-value = 4.423e-14shapiro.test(log(df2$shannon))##
## Shapiro-Wilk normality test
##
## data: log(df2$shannon)
## W = 0.84813, p-value < 2.2e-16# Run linear regression
summary(lm(tmao_log ~ shannon + sex*age + cystatinc_bd1, df2))##
## Call:
## lm(formula = tmao_log ~ shannon + sex * age + cystatinc_bd1,
## data = df2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.56039 -0.30824 -0.01383 0.28203 1.82041
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.307376 0.302115 -1.017 0.309666
## shannon 0.172077 0.044785 3.842 0.000145 ***
## sex2 -0.431212 0.163883 -2.631 0.008888 **
## age -0.001198 0.002854 -0.420 0.674953
## cystatinc_bd1 0.695472 0.194619 3.573 0.000402 ***
## sex2:age 0.009960 0.003812 2.613 0.009374 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4914 on 347 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1311, Adjusted R-squared: 0.1185
## F-statistic: 10.47 on 5 and 347 DF, p-value: 2.264e-09# Check distribution
shapiro.test(df2$faith_pd) # normal##
## Shapiro-Wilk normality test
##
## data: df2$faith_pd
## W = 0.99699, p-value = 0.7601# Run linear regression
summary(lm(tmao_log ~ faith_pd + sex*age + cystatinc_bd1, df2))##
## Call:
## lm(formula = tmao_log ~ faith_pd + sex * age + cystatinc_bd1,
## data = df2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.67973 -0.30954 -0.02643 0.29712 1.79212
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1109111 0.2293375 0.484 0.628964
## faith_pd 0.0653056 0.0170657 3.827 0.000154 ***
## sex2 -0.3998769 0.1636350 -2.444 0.015035 *
## age -0.0009222 0.0028409 -0.325 0.745677
## cystatinc_bd1 0.6827331 0.1946261 3.508 0.000511 ***
## sex2:age 0.0097816 0.0038107 2.567 0.010680 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4915 on 347 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1308, Adjusted R-squared: 0.1183
## F-statistic: 10.44 on 5 and 347 DF, p-value: 2.39e-09# Check distribution
shapiro.test(df2$pielou_e) # not normal, proceed with caution##
## Shapiro-Wilk normality test
##
## data: df2$pielou_e
## W = 0.81544, p-value < 2.2e-16# Run linear regression
summary(lm(tmao_log ~ pielou_e + sex*age + cystatinc_bd1, df2))##
## Call:
## lm(formula = tmao_log ~ pielou_e + sex * age + cystatinc_bd1,
## data = df2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.59096 -0.29261 -0.00173 0.28921 1.87433
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2456861 0.3759449 -0.654 0.513856
## pielou_e 1.1175333 0.4316197 2.589 0.010026 *
## sex2 -0.4311371 0.1660659 -2.596 0.009827 **
## age -0.0001399 0.0028627 -0.049 0.961054
## cystatinc_bd1 0.6877669 0.1968057 3.495 0.000536 ***
## sex2:age 0.0097826 0.0038562 2.537 0.011623 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.497 on 347 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1113, Adjusted R-squared: 0.09846
## F-statistic: 8.689 on 5 and 347 DF, p-value: 8.927e-08# Check distribution
shapiro.test(df2$observed_otus) # normal##
## Shapiro-Wilk normality test
##
## data: df2$observed_otus
## W = 0.99609, p-value = 0.5367# Run linear regression
summary(lm(tmao_log ~ observed_otus + sex*age + cystatinc_bd1, df2))##
## Call:
## lm(formula = tmao_log ~ observed_otus + sex * age + cystatinc_bd1,
## data = df2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.71047 -0.30903 -0.02197 0.28130 1.83166
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1663387 0.2189965 0.760 0.448039
## observed_otus 0.0033524 0.0008354 4.013 7.35e-05 ***
## sex2 -0.3924683 0.1632997 -2.403 0.016769 *
## age -0.0011621 0.0028421 -0.409 0.682870
## cystatinc_bd1 0.6889571 0.1942392 3.547 0.000443 ***
## sex2:age 0.0096736 0.0038019 2.544 0.011380 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4905 on 347 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1343, Adjusted R-squared: 0.1218
## F-statistic: 10.76 on 5 and 347 DF, p-value: 1.233e-09Alpha Diversity Plots
Make plots that demonstrate the relationship between TMAO and the alpha-diversity metrics.
# Shannon
y_variable <- "Shannon"
x_variable <- "Natural Log of TMAO (uM)"
mdn_cut <- log(median(df2$tmao.x))
# Plot
ggplot(df2, aes(x=tmao_log, y=shannon)) +
geom_point(aes(shape=bmi_cat, color = age_cat)) +
geom_vline(xintercept = mdn_cut, linetype = "dashed") +
geom_smooth(method = "lm", se = FALSE, fullrange = F) +
theme_minimal() +
ylab(y_variable) +
xlab(x_variable) +
labs(color = "Age bin", shape = "BMI bin") +
scale_color_brewer(palette = "Dark2")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
# Faith's Phylogenetic Diversity
y_variable <- "Faith's Phylogenetic Diversity"
x_variable <- "Natural Log of TMAO (uM)"
mdn_cut <- log(median(df2$tmao.x))
# Plot
ggplot(df2, aes(x=tmao_log, y=faith_pd)) +
geom_point(aes(shape=bmi_cat, color = age_cat)) +
geom_vline(xintercept = mdn_cut, linetype = "dashed") +
geom_smooth(method = "lm", se = FALSE, fullrange = F) +
theme_minimal() +
ylab(y_variable) +
xlab(x_variable) +
labs(color = "Age bin", shape = "BMI bin") +
scale_color_brewer(palette = "Dark2")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).
## Warning: Removed 2 rows containing missing values (geom_point).
# Pielou's Evenness
y_variable <- "Pielou's Evenness"
x_variable <- "Natural Log of TMAO (uM)"
mdn_cut <- log(median(df2$tmao.x))
# Plot
ggplot(df2, aes(x=tmao_log, y=pielou_e)) +
geom_point(aes(shape=bmi_cat, color = age_cat)) +
geom_vline(xintercept = mdn_cut, linetype = "dashed") +
geom_smooth(method = "lm", se = FALSE, fullrange = F) +
theme_minimal() +
ylab(y_variable) +
xlab(x_variable) +
labs(color = "Age bin", shape = "BMI bin") +
scale_color_brewer(palette = "Dark2")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).
## Warning: Removed 2 rows containing missing values (geom_point).
# Observed OTUs
y_variable <- "Observed OTUs"
x_variable <- "Natural Log of TMAO (uM)"
mdn_cut <- log(median(df2$tmao.x))
# Plot
ggplot(df2, aes(x=tmao_log, y=observed_otus)) +
geom_point(aes(shape=bmi_cat, color = age_cat)) +
geom_vline(xintercept = mdn_cut, linetype = "dashed") +
geom_smooth(method = "lm", se = FALSE, fullrange = F) +
theme_minimal() +
ylab(y_variable) +
xlab(x_variable) +
labs(color = "Age bin", shape = "BMI bin") +
scale_color_brewer(palette = "Dark2")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).
## Warning: Removed 2 rows containing missing values (geom_point).
Beta Diversity Analysis
# PCoA
PCoA_WU <- ordinate(PSOtmao1G, "PCoA", "wunifrac")
PCoA_UnWU <- ordinate(PSOtmao1G, "PCoA", "uunifrac")
PCoA_BC <- ordinate(PSOtmao1G, "PCoA", "bray")
# Calculate distance matrix
dismax_wu = phyloseq::distance(PSOtmao1G, method="wunifrac", type = "samples")
dismax_uwu = phyloseq::distance(PSOtmao1G, method="unifrac", type = "samples")
dismax_bc = phyloseq::distance(PSOtmao1G, method="bray", type = "samples")Save
save(dismax_wu, file = "../../data/processed/microbiome/diversity/PSOtmao1G_dismax_WU.RData")
save(dismax_uwu, file = "../../data/processed/microbiome/diversity/PSOtmao1G_dismax_UWU.RData")
save(dismax_bc, file = "../../data/processed/microbiome/diversity/PSOtmao1G_dismax_BC.RData")# Get sample_data
df <- as(sample_data(PSOtmao1G), "data.frame")
df$tmao_mdn <- relevel(df$tmao_mdn, ref = "below")
#~~~~~~~~~~~
# Weighted
#~~~~~~~~~~~
# Beta dispersion
bdis <- betadisper(dismax_wu, df$tmao_mdn)
permutest(bdis) # NS, good##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 1 0.0356 0.035619 3.0063 999 0.091 .
## Residuals 353 4.1824 0.011848
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1boxplot(bdis, las = 2)
# ADONIS (perMANOVA) analysis
adonis2(dismax_wu ~ tmao_log + sex + age + bmi_final.y, data=df, method = "wunifrac", perm=999) # .025## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = dismax_wu ~ tmao_log + sex + age + bmi_final.y, data = df, permutations = 999, method = "wunifrac")
## Df SumOfSqs R2 F Pr(>F)
## tmao_log 1 0.1372 0.00839 3.0523 0.024 *
## sex 1 0.3377 0.02066 7.5156 0.001 ***
## age 1 0.0987 0.00604 2.1973 0.074 .
## bmi_final.y 1 0.0465 0.00285 1.0353 0.350
## Residual 350 15.7279 0.96207
## Total 354 16.3481 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#adonis2(tmao_log ~ dismax_wu + sex*age + cystatinc_bd1, data=df, method = "wunifrac", perm=999)
#~~~~~~~~~~~
# Unweighted
#~~~~~~~~~~~
# Beta dispersion
bdis <- betadisper(dismax_uwu, df$tmao_mdn)
permutest(bdis) # NS, good##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 1 0.0000 0.0000023 7e-04 999 0.974
## Residuals 353 1.1323 0.0032077boxplot(bdis, las = 2)
# ADONIS (perMANOVA) analysis
adonis2(dismax_uwu ~ tmao_log + sex + age + bmi_final.y, data=df, method = "unifrac", perm=999) # .001## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = dismax_uwu ~ tmao_log + sex + age + bmi_final.y, data = df, permutations = 999, method = "unifrac")
## Df SumOfSqs R2 F Pr(>F)
## tmao_log 1 0.2577 0.01541 5.6065 0.001 ***
## sex 1 0.1479 0.00885 3.2181 0.002 **
## age 1 0.1017 0.00609 2.2137 0.023 *
## bmi_final.y 1 0.1254 0.00750 2.7287 0.006 **
## Residual 350 16.0868 0.96215
## Total 354 16.7196 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#adonis2(tmao_log ~ dismax_uwu + sex*age + cystatinc_bd1, data=df, method = "unifrac", perm=999)
#~~~~~~~~~~~
# BrayCurtis
#~~~~~~~~~~~
# Beta dispersion
bdis <- betadisper(dismax_bc, df$tmao_mdn)
permutest(bdis) # NS, good##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 1 0.0455 0.045457 4.3235 999 0.029 *
## Residuals 353 3.7114 0.010514
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1boxplot(bdis, las = 2)
# ADONIS (perMANOVA) analysis
adonis2(dismax_bc ~ tmao_log + sex + age + bmi_final.y, data=df, method = "bray", perm=999) # .017## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = dismax_bc ~ tmao_log + sex + age + bmi_final.y, data = df, permutations = 999, method = "bray")
## Df SumOfSqs R2 F Pr(>F)
## tmao_log 1 0.358 0.00806 2.9328 0.004 **
## sex 1 0.694 0.01561 5.6813 0.001 ***
## age 1 0.400 0.00901 3.2801 0.003 **
## bmi_final.y 1 0.257 0.00579 2.1074 0.023 *
## Residual 350 42.725 0.96153
## Total 354 44.434 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#adonis2(tmao_log ~ dismax_bc + sex*age + cystatinc_bd1, data=df, method = "bray", perm=999) ANCOM
ANCOM2 no-covariate analysis
source("../../Rscripts/ancom_v2.1.R")##
## Attaching package: 'nlme'## The following object is masked from 'package:dplyr':
##
## collapse## The following object is masked from 'package:IRanges':
##
## collapse## Welcome to compositions, a package for compositional data analysis.
## Find an intro with "? compositions"##
## Attaching package: 'compositions'## The following objects are masked from 'package:IRanges':
##
## cor, cov, var## The following objects are masked from 'package:S4Vectors':
##
## cor, cov, var## The following objects are masked from 'package:BiocGenerics':
##
## normalize, var## The following objects are masked from 'package:stats':
##
## anova, cor, cov, dist, var## The following objects are masked from 'package:base':
##
## %*%, norm, scale, scale.default# OTU table
MicroData <- otu_table(PSOtmao1G)
MicroData <- as.data.frame(as.matrix(MicroData), stringsAsFactors = F)
# Add 1
#MicroData2 <- MicroData +1
#MicroData2 <- as.data.frame(as.matrix(t(MicroData2),stringsAsFactors = F))
#dim(MicroData2) # 103 x 98
# Load the phenotype data
#------------------------
# Perform ANCOM analysis
# Step 1: Data preprocessing
# OTU data
otu_data <- otu_table(PSOtmao1G)
otu_data <- as.data.frame(as.matrix(otu_data), stringsAsFactors = F)
otu_id <- rownames(otu_data)
# Metadata
#df <- readRDS("../data_processed/mapping/Biocrates_mapping_BD1_210809.rds")
#df$p.Cresol.SO4_bd1_mdn <- plyr::revalue(df$p.Cresol.SO4_bd1_mdn, c("0" = "< Median", "1" = "> Median"))
# Subset
MetaData2 <- df2 %>% dplyr::select(c(tmao_mdn, age, sex, bmi_final))
MetaData2$Sample.ID <- rownames(MetaData2)
feature_table = otu_data; sample_var = "Sample.ID"; group_var = NULL
out_cut = 0.05; zero_cut = 0.95; lib_cut = 1000; neg_lb = FALSE # make zero_cut=0.95 to match criteria used for other metabolites
# Function
prepro = feature_table_pre_process(feature_table = feature_table,
meta_data = MetaData2,
sample_var = sample_var,
group_var = NULL,
out_cut = out_cut,
zero_cut = zero_cut,
lib_cut = lib_cut,
neg_lb = neg_lb)
# Step 2: ANCOM
feature_table = prepro$feature_table
meta_data = prepro$meta_data
struc_zero = prepro$structure_zeros
# Define variables for ANCOM
main_var = "tmao_mdn"; p_adj_method = "BH"; alpha = 0.05
adj_formula = NULL ; rand_formula = NULL
# Run ANCCOM
t_start = Sys.time()
res = ANCOM(feature_table = feature_table,
meta_data = meta_data,
struc_zero = struc_zero,
main_var = main_var,
p_adj_method = p_adj_method,
alpha = alpha,
adj_formula = adj_formula,
rand_formula = rand_formula)
t_end = Sys.time()
t_run = t_end - t_start
t_run # around 5s## Time difference of 6.444072 secs# Results
res$out## taxa_id W detected_0.9 detected_0.8 detected_0.7
## 1 77a72e43b8d0143ee2d29c79be1b00da 0 FALSE FALSE FALSE
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## 44 e0b0817162b44d0e17a1c50486839924 1 FALSE FALSE FALSE
## 45 6960eba3db7d4d863d042ab497d7481a 0 FALSE FALSE FALSE
## 46 8512cc461fa8b253841200871faba531 0 FALSE FALSE FALSE
## 47 a38b6324c57e2ef3c842180166aeb60f 0 FALSE FALSE FALSE
## 48 172049e46605909f99651e4d0efb8578 1 FALSE FALSE FALSE
## 49 6e13c195d4554dd8cf4923df9decd183 0 FALSE FALSE FALSE
## 50 d2a695aa271adf072749a729fd96a731 3 FALSE FALSE FALSE
## 51 520eb0909e5157503fbed62cbb4e73ca 20 FALSE FALSE FALSE
## 52 42de4e368cca3ff50954f82c12dd5315 3 FALSE FALSE FALSE
## 53 f966e124604e0e32b209b88df6e42cd4 1 FALSE FALSE FALSE
## 54 f7c08b0d8e574f7c089b95e0e4344d5e 0 FALSE FALSE FALSE
## 55 1dda53416f231a3345668df39d4ae780 2 FALSE FALSE FALSE
## 56 8c6b152535efbebf12179855cb31b8d8 0 FALSE FALSE FALSE
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## 60 8455a8f642c4b847f76baa31664bcfaa 0 FALSE FALSE FALSE
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## 67 e00f85cb3452e37943500e86afb268f4 0 FALSE FALSE FALSE
## detected_0.6
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## 7 FALSE
## 8 FALSE
## 9 FALSE
## 10 FALSE
## 11 FALSE
## 12 FALSE
## 13 FALSE
## 14 FALSE
## 15 FALSE
## 16 FALSE
## 17 FALSE
## 18 FALSE
## 19 FALSE
## 20 FALSE
## 21 FALSE
## 22 FALSE
## 23 TRUE
## 24 FALSE
## 25 FALSE
## 26 FALSE
## 27 FALSE
## 28 FALSE
## 29 FALSE
## 30 FALSE
## 31 FALSE
## 32 FALSE
## 33 FALSE
## 34 FALSE
## 35 FALSE
## 36 FALSE
## 37 FALSE
## 38 FALSE
## 39 FALSE
## 40 TRUE
## 41 FALSE
## 42 FALSE
## 43 FALSE
## 44 FALSE
## 45 FALSE
## 46 FALSE
## 47 FALSE
## 48 FALSE
## 49 FALSE
## 50 FALSE
## 51 FALSE
## 52 FALSE
## 53 FALSE
## 54 FALSE
## 55 FALSE
## 56 FALSE
## 57 FALSE
## 58 FALSE
## 59 FALSE
## 60 FALSE
## 61 FALSE
## 62 FALSE
## 63 FALSE
## 64 FALSE
## 65 FALSE
## 66 FALSE
## 67 FALSEres$fig$data## taxa_id x
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## y zero_ind
## 77a72e43b8d0143ee2d29c79be1b00da 0 No
## f1d7ac8c18c1d8144a00e4d785c86e4e 0 No
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## 520eb0909e5157503fbed62cbb4e73ca 20 No
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## 8c6b152535efbebf12179855cb31b8d8 0 No
## 8aa67fe08404e3d574aafb6622786c0f 0 No
## ec70d97b11476164cbd828e126ad10da 0 No
## 5c4ca852b40641b3eb0ad23e69bb6583 1 No
## 8455a8f642c4b847f76baa31664bcfaa 0 No
## ebe91a7d912f432d8726b0cef99d18c3 1 No
## 9a299c48d0e3ddea2122806528070d6e 0 No
## e4ae256bb51896c21795f743dc9ed9dd 0 No
## 7a297761643e32391ea7cd03b0995e62 0 No
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## 8f5ceded1cd7e86b8271d3fab24d322a 0 No
## e00f85cb3452e37943500e86afb268f4 0 No# Step 3: Volcano Plot
# Number of taxa except structural zeros
n_taxa = ifelse(is.null(struc_zero), nrow(feature_table), sum(apply(struc_zero, 1, sum) == 0))
# Cutoff values for declaring differentially abundant taxa
cut_off = c(0.9 * (n_taxa -1), 0.8 * (n_taxa -1), 0.7 * (n_taxa -1), 0.6 * (n_taxa -1))
names(cut_off) = c("detected_0.9", "detected_0.8", "detected_0.7", "detected_0.6")
# Annotation data
dat_ann = data.frame(x = min(res$fig$data$x), y = cut_off["detected_0.7"], label = "W[0.7]")
fig = res$fig +
geom_hline(yintercept = cut_off["detected_0.7"], linetype = "dashed") +
geom_text(data = dat_ann, aes(x = x, y = y, label = label),
size = 4, vjust = -0.5, hjust = 0, color = "orange", parse = TRUE)
fig 
# ORGANIZE PLOT
TaxaData2 <- readRDS("../../data/processed/microbiome/phyloseq_inputs/merged-taxonomy-dada2-retrained.rds")
rownames(TaxaData2) <- TaxaData2$Feature.ID## Warning: Setting row names on a tibble is deprecated.ancomRes <- res$out
test <- merge(ancomRes, TaxaData2, by.x = "taxa_id", by.y = "Feature.ID", all.x = TRUE, all.y = FALSE)
# Make plot
tempdata <- test[test$detected_0.6 == TRUE,] # 0.6 but can make 0.7
asv_id <- unique(tempdata$taxa_id)
MicroData4 <- feature_table[row.names(feature_table) %in% asv_id,]
# Rename to the taxonomy
#MicroData5 <- as.data.frame(as.matrix(t(MicroData4)))
TaxaData3 <- TaxaData2 %>% dplyr::select(Order, Family, Genus)
# Make a unique naming column that incorporates multiple levels of taxonomy
TaxaData3$OrderFamilyGenus <- paste(TaxaData3$Order, TaxaData3$Family, TaxaData3$Genus)
rownames(TaxaData3) <- rownames(TaxaData2)## Warning: Setting row names on a tibble is deprecated.# Merge
MicroData5 <- merge(MicroData4, TaxaData3,
by=0, all.x = T, all.y = F)
row.names(MicroData5) <- MicroData5$OrderFamilyGenus
MicroData5 <- MicroData5 %>% dplyr::select(-Order, -Family, -Genus, -OrderFamilyGenus, -Row.names)
MicroData5 <- as.data.frame(as.matrix(t(MicroData5)))
# Merge with the phenotype data
MetaData3 <- MetaData2 %>% dplyr::select(tmao_mdn)
ggData <- merge(MetaData3, MicroData5, by =0)
row.names(ggData) <- ggData$Row.names
ggData <- ggData %>% dplyr::select(-Row.names)
# Gather the data
library(tidyr)##
## Attaching package: 'tidyr'## The following object is masked from 'package:S4Vectors':
##
## expandggData2 <- gather(ggData,
key = "Taxa",
value = "Abundance",
-tmao_mdn)
ggData2$Abundance <- log(ggData2$Abundance)
# Plot it
p <- ggplot(data = ggData2,
aes(x = Taxa,
y = Abundance,
fill = tmao_mdn),
alpha = 0.1) +
geom_boxplot(lwd=.25) +
xlab(NULL) +
ylab("Log of Taxa Abundance") +
theme_bw() + # note order matters here - need bw to be before theme()
theme(axis.text.y = element_text( hjust = 1),
axis.text.x = element_text(angle = 60, hjust = 1),
text=element_text(size=11)) +
scale_fill_manual(values=c("#2D708EFF", "#B8DE29FF")) +
#scale_fill_viridis(discrete = TRUE) +
#coord_flip() +
labs(fill = "TMAO \n Classification")
p## Warning: Removed 166 rows containing non-finite values (stat_boxplot).
ANCOM2 covariate analysis
ANCOM2 lets you include covariates into the analysis. If we add the same covariates as in the DESeq2 analysis, do we get similar results?
source("../../Rscripts/ancom_v2.1.R")
# OTU table
MicroData <- otu_table(PSOtmao1G)
MicroData <- as.data.frame(as.matrix(MicroData), stringsAsFactors = F)
# Add 1
#MicroData2 <- MicroData +1
#MicroData2 <- as.data.frame(as.matrix(t(MicroData2),stringsAsFactors = F))
#dim(MicroData2) # 103 x 98
# Load the phenotype data
#------------------------
# Perform ANCOM analysis
# Step 1: Data preprocessing
# OTU data
otu_data <- otu_table(PSOtmao1G)
otu_data <- as.data.frame(as.matrix(otu_data), stringsAsFactors = F)
otu_id <- rownames(otu_data)
# Metadata
#df <- readRDS("../data_processed/mapping/Biocrates_mapping_BD1_210809.rds")
#df$p.Cresol.SO4_bd1_mdn <- plyr::revalue(df$p.Cresol.SO4_bd1_mdn, c("0" = "< Median", "1" = "> Median"))
# Subset
MetaData2 <- df2 %>% dplyr::select(c(tmao_mdn, age, sex, bmi_final))
MetaData2$Sample.ID <- rownames(MetaData2)
feature_table = otu_data; sample_var = "Sample.ID"; group_var = NULL
out_cut = 0.05; zero_cut = 0.95; lib_cut = 1000; neg_lb = FALSE # make zero_cut=0.95 to match criteria used for other metabolites
# Function
prepro = feature_table_pre_process(feature_table = feature_table,
meta_data = MetaData2,
sample_var = sample_var,
group_var = NULL,
out_cut = out_cut,
zero_cut = zero_cut,
lib_cut = lib_cut,
neg_lb = neg_lb)
# Step 2: ANCOM
feature_table = prepro$feature_table
meta_data = prepro$meta_data
struc_zero = prepro$structure_zeros
# Define variables for ANCOM
main_var = "tmao_mdn"; p_adj_method = "BH"; alpha = 0.05
adj_formula = c("age + sex + bmi_final"); rand_formula = NULL
# Run ANCCOM
t_start = Sys.time()
res = ANCOM(feature_table = feature_table,
meta_data = meta_data,
struc_zero = struc_zero,
main_var = main_var,
p_adj_method = p_adj_method,
alpha = alpha,
adj_formula = adj_formula,
rand_formula = rand_formula)
t_end = Sys.time()
t_run = t_end - t_start
t_run # around 5s## Time difference of 6.096518 secs# Results
res$out## taxa_id W detected_0.9 detected_0.8 detected_0.7
## 1 77a72e43b8d0143ee2d29c79be1b00da 0 FALSE FALSE FALSE
## 2 f1d7ac8c18c1d8144a00e4d785c86e4e 0 FALSE FALSE FALSE
## 3 7958dd98cfbbdc096240cd1dc4531423 0 FALSE FALSE FALSE
## 4 05404c9fdf9f3f334eb618bac3f434f6 0 FALSE FALSE FALSE
## 5 06105df60508c2ed24a54f1b8ed64e49 0 FALSE FALSE FALSE
## 6 7ce12788fd2f8479636e23aae67e09c8 0 FALSE FALSE FALSE
## 7 3f6503536588cb53c493d4c42e94a338 1 FALSE FALSE FALSE
## 8 bb1b75f41ff9c9db1d1de41e8388eb52 0 FALSE FALSE FALSE
## 9 1efe70e365249c0d2fc28580b6ba0529 0 FALSE FALSE FALSE
## 10 098c3bbd8234f4ac198297ac0bde957d 0 FALSE FALSE FALSE
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## 12 3f5533292a655d0f54a64560d65e823b 0 FALSE FALSE FALSE
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## 15 a4bb7f5133099cd74c3817b8d3383326 1 FALSE FALSE FALSE
## 16 5be4e26b904b9d711eb829785568ff92 0 FALSE FALSE FALSE
## 17 a41766e0533ab64a9966f362a7938478 0 FALSE FALSE FALSE
## 18 4b11c7e76e289279c460920e677924c4 0 FALSE FALSE FALSE
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## 21 f0c88652fcc37b16b7820628f7711ce2 0 FALSE FALSE FALSE
## 22 2e3414cd356e335e4f675efeb43938b3 0 FALSE FALSE FALSE
## 23 8fabb679415fe6a9969015076873e211 45 FALSE FALSE FALSE
## 24 7363aac88b5325ac49cb469e284e10dd 0 FALSE FALSE FALSE
## 25 2dc2cf60102ff00cd077f2304ec84d06 0 FALSE FALSE FALSE
## 26 cbb17c39cfea0aa843212c70619d1316 0 FALSE FALSE FALSE
## 27 555a3619c7746cdad6de2b1c181e791c 0 FALSE FALSE FALSE
## 28 c1bb8cdcab0662dec8ca827a53614d5e 0 FALSE FALSE FALSE
## 29 ebf61ede654f305596908efa9a8ddf54 1 FALSE FALSE FALSE
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## 31 a0798db0c868e9ad6089502956d68d87 0 FALSE FALSE FALSE
## 32 c5859b7f5d14b8c145fbc3ad583c70e8 3 FALSE FALSE FALSE
## 33 44bb844e62eae0ceadc4989cfd550b9b 0 FALSE FALSE FALSE
## 34 41712010fd4dd22c723e5490a0184a5d 1 FALSE FALSE FALSE
## 35 5b526c732aa8f6b7ddbf6b35925d5011 0 FALSE FALSE FALSE
## 36 b180a2455b236c103cf0bfe620095736 18 FALSE FALSE FALSE
## 37 590455138b9a5855dcb8f43167711f49 0 FALSE FALSE FALSE
## 38 e1a2800b24cdf9779b28dc897cddb12a 1 FALSE FALSE FALSE
## 39 f2cd13764c9588bd921f3a53138830da 0 FALSE FALSE FALSE
## 40 a7eb050200d9b2408521cc7ae42327ba 37 FALSE FALSE FALSE
## 41 cab39ce0fdcec18719b175b8181bd917 0 FALSE FALSE FALSE
## 42 4a4d11bb32e68079b4c71483bf0fcf36 28 FALSE FALSE FALSE
## 43 d5c6c4e5dc8f5bc4536e4fc361eca630 0 FALSE FALSE FALSE
## 44 e0b0817162b44d0e17a1c50486839924 0 FALSE FALSE FALSE
## 45 6960eba3db7d4d863d042ab497d7481a 0 FALSE FALSE FALSE
## 46 8512cc461fa8b253841200871faba531 0 FALSE FALSE FALSE
## 47 a38b6324c57e2ef3c842180166aeb60f 0 FALSE FALSE FALSE
## 48 172049e46605909f99651e4d0efb8578 1 FALSE FALSE FALSE
## 49 6e13c195d4554dd8cf4923df9decd183 0 FALSE FALSE FALSE
## 50 d2a695aa271adf072749a729fd96a731 2 FALSE FALSE FALSE
## 51 520eb0909e5157503fbed62cbb4e73ca 2 FALSE FALSE FALSE
## 52 42de4e368cca3ff50954f82c12dd5315 3 FALSE FALSE FALSE
## 53 f966e124604e0e32b209b88df6e42cd4 0 FALSE FALSE FALSE
## 54 f7c08b0d8e574f7c089b95e0e4344d5e 0 FALSE FALSE FALSE
## 55 1dda53416f231a3345668df39d4ae780 0 FALSE FALSE FALSE
## 56 8c6b152535efbebf12179855cb31b8d8 0 FALSE FALSE FALSE
## 57 8aa67fe08404e3d574aafb6622786c0f 0 FALSE FALSE FALSE
## 58 ec70d97b11476164cbd828e126ad10da 0 FALSE FALSE FALSE
## 59 5c4ca852b40641b3eb0ad23e69bb6583 1 FALSE FALSE FALSE
## 60 8455a8f642c4b847f76baa31664bcfaa 0 FALSE FALSE FALSE
## 61 ebe91a7d912f432d8726b0cef99d18c3 0 FALSE FALSE FALSE
## 62 9a299c48d0e3ddea2122806528070d6e 0 FALSE FALSE FALSE
## 63 e4ae256bb51896c21795f743dc9ed9dd 0 FALSE FALSE FALSE
## 64 7a297761643e32391ea7cd03b0995e62 1 FALSE FALSE FALSE
## 65 73b4f14d16f02ee0ac82838166128de4 0 FALSE FALSE FALSE
## 66 8f5ceded1cd7e86b8271d3fab24d322a 0 FALSE FALSE FALSE
## 67 e00f85cb3452e37943500e86afb268f4 0 FALSE FALSE FALSE
## detected_0.6
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## 7 FALSE
## 8 FALSE
## 9 FALSE
## 10 FALSE
## 11 FALSE
## 12 FALSE
## 13 FALSE
## 14 FALSE
## 15 FALSE
## 16 FALSE
## 17 FALSE
## 18 FALSE
## 19 FALSE
## 20 FALSE
## 21 FALSE
## 22 FALSE
## 23 TRUE
## 24 FALSE
## 25 FALSE
## 26 FALSE
## 27 FALSE
## 28 FALSE
## 29 FALSE
## 30 FALSE
## 31 FALSE
## 32 FALSE
## 33 FALSE
## 34 FALSE
## 35 FALSE
## 36 FALSE
## 37 FALSE
## 38 FALSE
## 39 FALSE
## 40 FALSE
## 41 FALSE
## 42 FALSE
## 43 FALSE
## 44 FALSE
## 45 FALSE
## 46 FALSE
## 47 FALSE
## 48 FALSE
## 49 FALSE
## 50 FALSE
## 51 FALSE
## 52 FALSE
## 53 FALSE
## 54 FALSE
## 55 FALSE
## 56 FALSE
## 57 FALSE
## 58 FALSE
## 59 FALSE
## 60 FALSE
## 61 FALSE
## 62 FALSE
## 63 FALSE
## 64 FALSE
## 65 FALSE
## 66 FALSE
## 67 FALSEres$fig$data## taxa_id x
## 77a72e43b8d0143ee2d29c79be1b00da 77a72e43b8d0143ee2d29c79be1b00da -0.057504260
## f1d7ac8c18c1d8144a00e4d785c86e4e f1d7ac8c18c1d8144a00e4d785c86e4e -0.083956508
## 7958dd98cfbbdc096240cd1dc4531423 7958dd98cfbbdc096240cd1dc4531423 -0.110756895
## 05404c9fdf9f3f334eb618bac3f434f6 05404c9fdf9f3f334eb618bac3f434f6 -0.101392861
## 06105df60508c2ed24a54f1b8ed64e49 06105df60508c2ed24a54f1b8ed64e49 -0.071860149
## 7ce12788fd2f8479636e23aae67e09c8 7ce12788fd2f8479636e23aae67e09c8 -0.150561356
## 3f6503536588cb53c493d4c42e94a338 3f6503536588cb53c493d4c42e94a338 0.020262603
## bb1b75f41ff9c9db1d1de41e8388eb52 bb1b75f41ff9c9db1d1de41e8388eb52 -0.133086185
## 1efe70e365249c0d2fc28580b6ba0529 1efe70e365249c0d2fc28580b6ba0529 -0.145390287
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## y zero_ind
## 77a72e43b8d0143ee2d29c79be1b00da 0 No
## f1d7ac8c18c1d8144a00e4d785c86e4e 0 No
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## 7ce12788fd2f8479636e23aae67e09c8 0 No
## 3f6503536588cb53c493d4c42e94a338 1 No
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## 5c4ca852b40641b3eb0ad23e69bb6583 1 No
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## 8f5ceded1cd7e86b8271d3fab24d322a 0 No
## e00f85cb3452e37943500e86afb268f4 0 No# Step 3: Volcano Plot
# Number of taxa except structural zeros
n_taxa = ifelse(is.null(struc_zero), nrow(feature_table), sum(apply(struc_zero, 1, sum) == 0))
# Cutoff values for declaring differentially abundant taxa
cut_off = c(0.9 * (n_taxa -1), 0.8 * (n_taxa -1), 0.7 * (n_taxa -1), 0.6 * (n_taxa -1))
names(cut_off) = c("detected_0.9", "detected_0.8", "detected_0.7", "detected_0.6")
# Annotation data
dat_ann = data.frame(x = min(res$fig$data$x), y = cut_off["detected_0.7"], label = "W[0.7]")
fig = res$fig +
geom_hline(yintercept = cut_off["detected_0.7"], linetype = "dashed") +
geom_text(data = dat_ann, aes(x = x, y = y, label = label),
size = 4, vjust = -0.5, hjust = 0, color = "orange", parse = TRUE)
fig 
# ORGANIZE PLOT
TaxaData2 <- readRDS("../../data/processed/microbiome/phyloseq_inputs/merged-taxonomy-dada2-retrained.rds")
rownames(TaxaData2) <- TaxaData2$Feature.ID## Warning: Setting row names on a tibble is deprecated.ancomRes <- res$out
test <- merge(ancomRes, TaxaData2, by.x = "taxa_id", by.y = "Feature.ID", all.x = TRUE, all.y = FALSE)
# Make plot
tempdata <- test[test$detected_0.6 == TRUE,] # 0.6 but can make 0.7
asv_id <- unique(tempdata$taxa_id)
MicroData4 <- feature_table[row.names(feature_table) %in% asv_id,]
# Rename to the taxonomy
#MicroData5 <- as.data.frame(as.matrix(t(MicroData4)))
TaxaData3 <- TaxaData2 %>% dplyr::select(Order, Family, Genus)
# Make a unique naming column that incorporates multiple levels of taxonomy
TaxaData3$OrderFamilyGenus <- paste(TaxaData3$Order, TaxaData3$Family, TaxaData3$Genus)
rownames(TaxaData3) <- rownames(TaxaData2)## Warning: Setting row names on a tibble is deprecated.# Merge
MicroData5 <- merge(MicroData4, TaxaData3,
by=0, all.x = T, all.y = F)
row.names(MicroData5) <- MicroData5$OrderFamilyGenus
MicroData5 <- MicroData5 %>% dplyr::select(-Order, -Family, -Genus, -OrderFamilyGenus, -Row.names)
MicroData5 <- as.data.frame(as.matrix(t(MicroData5)))
# Merge with the phenotype data
MetaData3 <- MetaData2 %>% dplyr::select(tmao_mdn)
ggData <- merge(MetaData3, MicroData5, by =0)
row.names(ggData) <- ggData$Row.names
ggData <- ggData %>% dplyr::select(-Row.names)
# Gather the data
library(tidyr)
ggData2 <- gather(ggData,
key = "Taxa",
value = "Abundance",
-tmao_mdn)
ggData2$Abundance <- log(ggData2$Abundance)
# Plot it
p <- ggplot(data = ggData2,
aes(x = Taxa,
y = Abundance,
fill = tmao_mdn),
alpha = 0.1) +
geom_boxplot(lwd=.25) +
xlab(NULL) +
ylab("Log of Taxa Abundance") +
theme_bw() + # note order matters here - need bw to be before theme()
theme(axis.text.y = element_text( hjust = 1),
axis.text.x = element_text(angle = 60, hjust = 1),
text=element_text(size=11)) +
scale_fill_manual(values=c("#2D708EFF", "#B8DE29FF")) +
#scale_fill_viridis(discrete = TRUE) +
#coord_flip() +
labs(fill = "TMAO \n Classification")
p## Warning: Removed 158 rows containing non-finite values (stat_boxplot).
# Stats for paper
Take a TMAO centric view point. So for the following questions, consider the whole group and the TMAO quantiles/tertiles.
# Get sample data groupings
sampleData <- as.data.frame(sample_data(PSOtmao1))
sampleData <- subset(sampleData, select = c(tmao_mdn, tmao_quantile, tmao_tertile, tmao))
# Make lists of participants in each group
tertile1 <- rownames(sampleData[sampleData$tmao_tertile == "Tertile1",])
tertile2 <- rownames(sampleData[sampleData$tmao_tertile == "Tertile2",])
tertile3 <- rownames(sampleData[sampleData$tmao_tertile == "Tertile3",])
# Get OTU table
otuTbl_PSO1 <- as.data.frame(otu_table(PSOtmao1))
otuTbl_PSO1 <- as.data.frame(t(otuTbl_PSO1)) # subjects to rows
# Get tax table
taxaData <- as.data.frame(tax_table(PSOtmao1))
taxaData$PCOFG <- paste0(taxaData$Phylum, taxaData$Class, taxaData$Order, taxaData$Family, taxaData$Genus)How many OTUs (not glommed) in analysis?
#~~~~~~~~~~
# Tertile 1
#~~~~~~~~~~
# What is the average number of OTUs per participant in tertile 1?
taxa_occurrences_T1 <- sapply(otuTbl_PSO1[row.names(otuTbl_PSO1) %in% tertile1,], function(x) sum(x>0))
summary(taxa_occurrences_T1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 9.00 17.00 28.47 41.00 117.00otuTbl_PSO1_smpCol <- as.data.frame(t(otuTbl_PSO1))
ppl_occurrences_T1 <- sapply(otuTbl_PSO1_smpCol[colnames(otuTbl_PSO1_smpCol) %in% tertile1,], function(x) sum(x>0))
summary(ppl_occurrences_T1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 13.00 37.00 44.00 44.34 51.00 72.00#~~~~~~~~~~
# Tertile 2
#~~~~~~~~~~
# What is the average number of OTUs per participant in tertile 1?
taxa_occurrences_T2 <- sapply(otuTbl_PSO1[row.names(otuTbl_PSO1) %in% tertile2,], function(x) sum(x>0))
summary(taxa_occurrences_T2)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.00 11.00 20.00 30.85 44.00 118.00otuTbl_PSO1_smpCol <- as.data.frame(t(otuTbl_PSO1))
ppl_occurrences_T2 <- sapply(otuTbl_PSO1_smpCol[colnames(otuTbl_PSO1_smpCol) %in% tertile2,], function(x) sum(x>0))
summary(ppl_occurrences_T2)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.00 39.00 47.00 45.66 53.00 69.00#~~~~~~~~~~
# Tertile 3
#~~~~~~~~~~
# What is the average number of OTUs per participant in tertile 1?
taxa_occurrences_T3 <- sapply(otuTbl_PSO1[row.names(otuTbl_PSO1) %in% tertile3,], function(x) sum(x>0))
summary(taxa_occurrences_T3)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.00 10.00 20.00 31.46 44.25 117.00otuTbl_PSO1_smpCol <- as.data.frame(t(otuTbl_PSO1))
ppl_occurrences_T3 <- sapply(otuTbl_PSO1_smpCol[colnames(otuTbl_PSO1_smpCol) %in% tertile3,], function(x) sum(x>0))
summary(ppl_occurrences_T3)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 12.00 34.00 41.00 39.92 46.00 66.00How many OTUs are present in 5%, 25%, 50% of samples?
What is the firmicutes to bacteroidetes ratio per tertile? How common are Firmicutes? Mean, SD, range? How common are Bacteroidetes? Mean, SD, range?
PSOtmao1_Phylum <- tax_glom(PSOtmao1, "Phylum", NArm = TRUE)
PSOtmao1_Phylum_tertile1 <- subset_samples(PSOtmao1_Phylum, tmao_tertile == "Tertile1")
PSOtmao1_Phylum_tertile2 <- subset_samples(PSOtmao1_Phylum, tmao_tertile == "Tertile2")
PSOtmao1_Phylum_tertile3 <- subset_samples(PSOtmao1_Phylum, tmao_tertile == "Tertile3")
#~~~~~~~~~~
# Tertile 1
#~~~~~~~~~~
# Prevalence - how many samples the taxa is observed in
prevalence_Phy_tert1 = apply(X = otu_table(PSOtmao1_Phylum_tertile1),
MARGIN = ifelse(taxa_are_rows(PSOtmao1_Phylum_tertile1), yes = 1, no = 2),
FUN = function(x){sum(x > 0)})
prevalence_Phy_tert1 = data.frame(Prevalence = prevalence_Phy_tert1,
TotalAbundance = taxa_sums(PSOtmao1_Phylum_tertile1), # the total number of occurrences (even if only observed 50 times in 1 sample, the number would be 50)
tax_table(PSOtmao1_Phylum_tertile1))
# Firmicutes:Bacteroidetes Abundance Ratio
firm_bacter_t1 <- prevalence_Phy_tert1$TotalAbundance[prevalence_Phy_tert1$Phylum == " p__Firmicutes"] / prevalence_Phy_tert1$TotalAbundance[prevalence_Phy_tert1$Phylum == " p__Bacteroidetes"]
#~~~~~~~~~~
# Tertile 2
#~~~~~~~~~~
# Prevalence - how many samples the taxa is observed in
prevalence_Phy_tert2 = apply(X = otu_table(PSOtmao1_Phylum_tertile2),
MARGIN = ifelse(taxa_are_rows(PSOtmao1_Phylum_tertile2), yes = 1, no = 2),
FUN = function(x){sum(x > 0)})
prevalence_Phy_tert2 = data.frame(Prevalence = prevalence_Phy_tert2,
TotalAbundance = taxa_sums(PSOtmao1_Phylum_tertile2), # the total number of occurrences (even if only observed 50 times in 1 sample, the number would be 50)
tax_table(PSOtmao1_Phylum_tertile2))
firm_bacter_t2 <- prevalence_Phy_tert2$TotalAbundance[prevalence_Phy_tert2$Phylum == " p__Firmicutes"] / prevalence_Phy_tert2$TotalAbundance[prevalence_Phy_tert2$Phylum == " p__Bacteroidetes"]
#~~~~~~~~~~
# Tertile 3
#~~~~~~~~~~
# Prevalence - how many samples the taxa is observed in
prevalence_Phy_tert3 = apply(X = otu_table(PSOtmao1_Phylum_tertile3),
MARGIN = ifelse(taxa_are_rows(PSOtmao1_Phylum_tertile3), yes = 1, no = 2),
FUN = function(x){sum(x > 0)})
prevalence_Phy_tert3 = data.frame(Prevalence = prevalence_Phy_tert3,
TotalAbundance = taxa_sums(PSOtmao1_Phylum_tertile3), # the total number of occurrences (even if only observed 50 times in 1 sample, the number would be 50)
tax_table(PSOtmao1_Phylum_tertile3))
firm_bacter_t3 <- prevalence_Phy_tert3$TotalAbundance[prevalence_Phy_tert3$Phylum == " p__Firmicutes"] / prevalence_Phy_tert3$TotalAbundance[prevalence_Phy_tert3$Phylum == " p__Bacteroidetes"]
# Compare ratios
cat("Firmicutes:Bacteroidetes ratio tertile 1:", firm_bacter_t1)## Firmicutes:Bacteroidetes ratio tertile 1: 3.476189cat("Firmicutes:Bacteroidetes ratio tertile 2:", firm_bacter_t2)## Firmicutes:Bacteroidetes ratio tertile 2: 4.427447cat("Firmicutes:Bacteroidetes ratio tertile 3:", firm_bacter_t3)## Firmicutes:Bacteroidetes ratio tertile 3: 4.76323What is the % abundance of a given taxa? Get summary statistics per tertile.
Range percentage of taxa per person = n occurrences of bug/total occurrences of all bugs * 100
#~~~~~~~~~~
# Tertile 1
#~~~~~~~~~~
trans <- transform_sample_counts(PSOtmao1_Phylum_tertile1, function(x) x / sum(x))
melted <- psmelt(trans)
subdf <- melted[melted$Phylum == " p__Firmicutes", ]
cat("Summmary of Firmicute abundance in tertile 1:")## Summmary of Firmicute abundance in tertile 1:summary(subdf$Abundance)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.1953 0.7177 0.8451 0.7823 0.8963 0.9700#~~~~~~~~~~
# Tertile 2
#~~~~~~~~~~
trans <- transform_sample_counts(PSOtmao1_Phylum_tertile2, function(x) x / sum(x))
melted <- psmelt(trans)
subdf <- melted[melted$Phylum == " p__Firmicutes", ]
cat("Summmary of Firmicute abundance in tertile 2:")## Summmary of Firmicute abundance in tertile 2:summary(subdf$Abundance)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.3883 0.7556 0.8306 0.8030 0.8812 0.9995#~~~~~~~~~~
# Tertile 3
#~~~~~~~~~~
trans <- transform_sample_counts(PSOtmao1_Phylum_tertile3, function(x) x / sum(x))
melted <- psmelt(trans)
subdf <- melted[melted$Phylum == " p__Firmicutes", ]
cat("Summmary of Firmicute abundance in tertile 3:")## Summmary of Firmicute abundance in tertile 3:summary(subdf$Abundance)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.3361 0.7816 0.8399 0.8175 0.8994 0.9584#~~~~~~~~~~
# Tertile 1
#~~~~~~~~~~
trans <- transform_sample_counts(PSOtmao1_Phylum_tertile1, function(x) x / sum(x))
melted <- psmelt(trans)
subdf <- melted[melted$Phylum == " p__Bacteroidetes", ]
cat("Summmary of Bacteroidetes abundance in tertile 1:")## Summmary of Bacteroidetes abundance in tertile 1:summary(subdf$Abundance)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.005037 0.076650 0.135212 0.190595 0.258695 0.799678#~~~~~~~~~~
# Tertile 2
#~~~~~~~~~~
trans <- transform_sample_counts(PSOtmao1_Phylum_tertile2, function(x) x / sum(x))
melted <- psmelt(trans)
subdf <- melted[melted$Phylum == " p__Bacteroidetes", ]
cat("Summmary of Bacteroidetes abundance in tertile 2:")## Summmary of Bacteroidetes abundance in tertile 2:summary(subdf$Abundance)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0005031 0.0951410 0.1450016 0.1744789 0.2134875 0.5996346#~~~~~~~~~~
# Tertile 3
#~~~~~~~~~~
trans <- transform_sample_counts(PSOtmao1_Phylum_tertile3, function(x) x / sum(x))
melted <- psmelt(trans)
subdf <- melted[melted$Phylum == " p__Bacteroidetes", ]
cat("Summmary of Bacteroidetes abundance in tertile 3:")## Summmary of Bacteroidetes abundance in tertile 3:summary(subdf$Abundance)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.02117 0.08077 0.12667 0.15860 0.18713 0.66188Genus: What are the most common genus? Use the PSOtmao1G, subset to low-tertile, make prevalence data frame, sort in descending order. Repeat for other tertiles.
# Subset by tertiles
PSOtmao1_tertile1 <- subset_samples(PSOtmao1, tmao_tertile == "Tertile1")
PSOtmao1_tertile2 <- subset_samples(PSOtmao1, tmao_tertile == "Tertile2")
PSOtmao1_tertile3 <- subset_samples(PSOtmao1, tmao_tertile == "Tertile3")
# How many taxa are there per tertile?
cat("n taxa in tertile 1:", ntaxa(PSOtmao1_tertile1))## n taxa in tertile 1: 508cat("n taxa in tertile 2:", ntaxa(PSOtmao1_tertile2))## n taxa in tertile 2: 508cat("n taxa in tertile 3:", ntaxa(PSOtmao1_tertile3))## n taxa in tertile 3: 508# Now subset at the genus level to identify the most common genus
# Subset by tertiles (Genus)
PSOtmao1G_tertile1 <- subset_samples(PSOtmao1G, tmao_tertile == "Tertile1")
PSOtmao1G_tertile2 <- subset_samples(PSOtmao1G, tmao_tertile == "Tertile2")
PSOtmao1G_tertile3 <- subset_samples(PSOtmao1G, tmao_tertile == "Tertile3")
#~~~~~~~~~~
# Tertile 1
#~~~~~~~~~~
# Prevalence - how many samples the taxa is observed in
prevalence_tert1 = apply(X = otu_table(PSOtmao1G_tertile1),
MARGIN = ifelse(taxa_are_rows(PSOtmao1G_tertile1), yes = 1, no = 2),
FUN = function(x){sum(x > 0)})
prevalence_tert1 = data.frame(Prevalence = prevalence_tert1,
TotalAbundance = taxa_sums(PSOtmao1G_tertile1), # the total number of occurrences (even if only observed 50 times in 1 sample, the number would be 50)
tax_table(PSOtmao1G_tertile1))
#~~~~~~~~~~
# Tertile 2
#~~~~~~~~~~
# Prevalence - how many samples the taxa is observed in
prevalence_tert2 = apply(X = otu_table(PSOtmao1G_tertile2),
MARGIN = ifelse(taxa_are_rows(PSOtmao1G_tertile2), yes = 1, no = 2),
FUN = function(x){sum(x > 0)})
prevalence_tert2 = data.frame(Prevalence = prevalence_tert2,
TotalAbundance = taxa_sums(PSOtmao1G_tertile2), # the total number of occurrences (even if only observed 50 times in 1 sample, the number would be 50)
tax_table(PSOtmao1G_tertile2))
#~~~~~~~~~~
# Tertile 3
#~~~~~~~~~~
# Prevalence - how many samples the taxa is observed in
prevalence_tert3 = apply(X = otu_table(PSOtmao1G_tertile3),
MARGIN = ifelse(taxa_are_rows(PSOtmao1G_tertile3), yes = 1, no = 2),
FUN = function(x){sum(x > 0)})
prevalence_tert3 = data.frame(Prevalence = prevalence_tert3,
TotalAbundance = taxa_sums(PSOtmao1G_tertile3), # the total number of occurrences (even if only observed 50 times in 1 sample, the number would be 50)
tax_table(PSOtmao1G_tertile3))View and sort top 10
cat("Most abundant genus in T1:")## Most abundant genus in T1:head(prevalence_tert1[order(prevalence_tert1$TotalAbundance, decreasing= TRUE),], n = 10)## Prevalence TotalAbundance Kingdom
## e00f85cb3452e37943500e86afb268f4 119 317886 k__Bacteria
## f966e124604e0e32b209b88df6e42cd4 117 270923 k__Bacteria
## bb1b75f41ff9c9db1d1de41e8388eb52 119 246667 k__Bacteria
## 098c3bbd8234f4ac198297ac0bde957d 51 201995 k__Bacteria
## 5c4ca852b40641b3eb0ad23e69bb6583 118 162882 k__Bacteria
## 42de4e368cca3ff50954f82c12dd5315 117 156693 k__Bacteria
## 1dda53416f231a3345668df39d4ae780 118 102679 k__Bacteria
## 73b4f14d16f02ee0ac82838166128de4 117 70872 k__Bacteria
## e4ae256bb51896c21795f743dc9ed9dd 119 58906 k__Bacteria
## ebf61ede654f305596908efa9a8ddf54 95 49686 k__Bacteria
## Phylum Class
## e00f85cb3452e37943500e86afb268f4 p__Firmicutes c__Clostridia
## f966e124604e0e32b209b88df6e42cd4 p__Firmicutes c__Clostridia
## bb1b75f41ff9c9db1d1de41e8388eb52 p__Bacteroidetes c__Bacteroidia
## 098c3bbd8234f4ac198297ac0bde957d p__Bacteroidetes c__Bacteroidia
## 5c4ca852b40641b3eb0ad23e69bb6583 p__Firmicutes c__Clostridia
## 42de4e368cca3ff50954f82c12dd5315 p__Firmicutes c__Clostridia
## 1dda53416f231a3345668df39d4ae780 p__Firmicutes c__Clostridia
## 73b4f14d16f02ee0ac82838166128de4 p__Firmicutes c__Clostridia
## e4ae256bb51896c21795f743dc9ed9dd p__Firmicutes c__Clostridia
## ebf61ede654f305596908efa9a8ddf54 p__Firmicutes c__Erysipelotrichi
## Order Family
## e00f85cb3452e37943500e86afb268f4 o__Clostridiales f__Lachnospiraceae
## f966e124604e0e32b209b88df6e42cd4 o__Clostridiales f__Ruminococcaceae
## bb1b75f41ff9c9db1d1de41e8388eb52 o__Bacteroidales f__Bacteroidaceae
## 098c3bbd8234f4ac198297ac0bde957d o__Bacteroidales f__Prevotellaceae
## 5c4ca852b40641b3eb0ad23e69bb6583 o__Clostridiales f__Lachnospiraceae
## 42de4e368cca3ff50954f82c12dd5315 o__Clostridiales f__Ruminococcaceae
## 1dda53416f231a3345668df39d4ae780 o__Clostridiales f__Lachnospiraceae
## 73b4f14d16f02ee0ac82838166128de4 o__Clostridiales f__Lachnospiraceae
## e4ae256bb51896c21795f743dc9ed9dd o__Clostridiales f__Lachnospiraceae
## ebf61ede654f305596908efa9a8ddf54 o__Erysipelotrichales f__Erysipelotrichaceae
## Genus Species
## e00f85cb3452e37943500e86afb268f4 g__Blautia <NA>
## f966e124604e0e32b209b88df6e42cd4 g__Faecalibacterium <NA>
## bb1b75f41ff9c9db1d1de41e8388eb52 g__Bacteroides <NA>
## 098c3bbd8234f4ac198297ac0bde957d g__Prevotella <NA>
## 5c4ca852b40641b3eb0ad23e69bb6583 g__Roseburia <NA>
## 42de4e368cca3ff50954f82c12dd5315 g__Ruminococcus <NA>
## 1dda53416f231a3345668df39d4ae780 g__Coprococcus <NA>
## 73b4f14d16f02ee0ac82838166128de4 g__Clostridium <NA>
## e4ae256bb51896c21795f743dc9ed9dd g__[Ruminococcus] <NA>
## ebf61ede654f305596908efa9a8ddf54 g__ <NA>cat("Most abundant genus in T2:")## Most abundant genus in T2:head(prevalence_tert2[order(prevalence_tert2$TotalAbundance, decreasing= TRUE),], n = 10)## Prevalence TotalAbundance Kingdom
## e00f85cb3452e37943500e86afb268f4 118 334724 k__Bacteria
## f966e124604e0e32b209b88df6e42cd4 116 270587 k__Bacteria
## bb1b75f41ff9c9db1d1de41e8388eb52 118 218767 k__Bacteria
## 42de4e368cca3ff50954f82c12dd5315 118 189125 k__Bacteria
## 098c3bbd8234f4ac198297ac0bde957d 65 140748 k__Bacteria
## 5c4ca852b40641b3eb0ad23e69bb6583 116 136821 k__Bacteria
## 1dda53416f231a3345668df39d4ae780 117 122306 k__Bacteria
## 73b4f14d16f02ee0ac82838166128de4 118 60612 k__Bacteria
## ebe91a7d912f432d8726b0cef99d18c3 118 56238 k__Bacteria
## d2a695aa271adf072749a729fd96a731 118 55299 k__Bacteria
## Phylum Class
## e00f85cb3452e37943500e86afb268f4 p__Firmicutes c__Clostridia
## f966e124604e0e32b209b88df6e42cd4 p__Firmicutes c__Clostridia
## bb1b75f41ff9c9db1d1de41e8388eb52 p__Bacteroidetes c__Bacteroidia
## 42de4e368cca3ff50954f82c12dd5315 p__Firmicutes c__Clostridia
## 098c3bbd8234f4ac198297ac0bde957d p__Bacteroidetes c__Bacteroidia
## 5c4ca852b40641b3eb0ad23e69bb6583 p__Firmicutes c__Clostridia
## 1dda53416f231a3345668df39d4ae780 p__Firmicutes c__Clostridia
## 73b4f14d16f02ee0ac82838166128de4 p__Firmicutes c__Clostridia
## ebe91a7d912f432d8726b0cef99d18c3 p__Firmicutes c__Clostridia
## d2a695aa271adf072749a729fd96a731 p__Firmicutes c__Clostridia
## Order Family
## e00f85cb3452e37943500e86afb268f4 o__Clostridiales f__Lachnospiraceae
## f966e124604e0e32b209b88df6e42cd4 o__Clostridiales f__Ruminococcaceae
## bb1b75f41ff9c9db1d1de41e8388eb52 o__Bacteroidales f__Bacteroidaceae
## 42de4e368cca3ff50954f82c12dd5315 o__Clostridiales f__Ruminococcaceae
## 098c3bbd8234f4ac198297ac0bde957d o__Bacteroidales f__Prevotellaceae
## 5c4ca852b40641b3eb0ad23e69bb6583 o__Clostridiales f__Lachnospiraceae
## 1dda53416f231a3345668df39d4ae780 o__Clostridiales f__Lachnospiraceae
## 73b4f14d16f02ee0ac82838166128de4 o__Clostridiales f__Lachnospiraceae
## ebe91a7d912f432d8726b0cef99d18c3 o__Clostridiales f__Lachnospiraceae
## d2a695aa271adf072749a729fd96a731 o__Clostridiales f__Ruminococcaceae
## Genus Species
## e00f85cb3452e37943500e86afb268f4 g__Blautia <NA>
## f966e124604e0e32b209b88df6e42cd4 g__Faecalibacterium <NA>
## bb1b75f41ff9c9db1d1de41e8388eb52 g__Bacteroides <NA>
## 42de4e368cca3ff50954f82c12dd5315 g__Ruminococcus <NA>
## 098c3bbd8234f4ac198297ac0bde957d g__Prevotella <NA>
## 5c4ca852b40641b3eb0ad23e69bb6583 g__Roseburia <NA>
## 1dda53416f231a3345668df39d4ae780 g__Coprococcus <NA>
## 73b4f14d16f02ee0ac82838166128de4 g__Clostridium <NA>
## ebe91a7d912f432d8726b0cef99d18c3 g__ <NA>
## d2a695aa271adf072749a729fd96a731 g__Oscillospira <NA>cat("Most abundant genus in T3:")## Most abundant genus in T3:head(prevalence_tert3[order(prevalence_tert3$TotalAbundance, decreasing= TRUE),], n = 10)## Prevalence TotalAbundance Kingdom
## e00f85cb3452e37943500e86afb268f4 118 319013 k__Bacteria
## f966e124604e0e32b209b88df6e42cd4 117 244405 k__Bacteria
## 42de4e368cca3ff50954f82c12dd5315 117 208080 k__Bacteria
## bb1b75f41ff9c9db1d1de41e8388eb52 118 175200 k__Bacteria
## 098c3bbd8234f4ac198297ac0bde957d 62 156321 k__Bacteria
## 5c4ca852b40641b3eb0ad23e69bb6583 118 142357 k__Bacteria
## 1dda53416f231a3345668df39d4ae780 118 126744 k__Bacteria
## ebf61ede654f305596908efa9a8ddf54 106 64886 k__Bacteria
## d2a695aa271adf072749a729fd96a731 117 56040 k__Bacteria
## ebe91a7d912f432d8726b0cef99d18c3 118 55425 k__Bacteria
## Phylum Class
## e00f85cb3452e37943500e86afb268f4 p__Firmicutes c__Clostridia
## f966e124604e0e32b209b88df6e42cd4 p__Firmicutes c__Clostridia
## 42de4e368cca3ff50954f82c12dd5315 p__Firmicutes c__Clostridia
## bb1b75f41ff9c9db1d1de41e8388eb52 p__Bacteroidetes c__Bacteroidia
## 098c3bbd8234f4ac198297ac0bde957d p__Bacteroidetes c__Bacteroidia
## 5c4ca852b40641b3eb0ad23e69bb6583 p__Firmicutes c__Clostridia
## 1dda53416f231a3345668df39d4ae780 p__Firmicutes c__Clostridia
## ebf61ede654f305596908efa9a8ddf54 p__Firmicutes c__Erysipelotrichi
## d2a695aa271adf072749a729fd96a731 p__Firmicutes c__Clostridia
## ebe91a7d912f432d8726b0cef99d18c3 p__Firmicutes c__Clostridia
## Order Family
## e00f85cb3452e37943500e86afb268f4 o__Clostridiales f__Lachnospiraceae
## f966e124604e0e32b209b88df6e42cd4 o__Clostridiales f__Ruminococcaceae
## 42de4e368cca3ff50954f82c12dd5315 o__Clostridiales f__Ruminococcaceae
## bb1b75f41ff9c9db1d1de41e8388eb52 o__Bacteroidales f__Bacteroidaceae
## 098c3bbd8234f4ac198297ac0bde957d o__Bacteroidales f__Prevotellaceae
## 5c4ca852b40641b3eb0ad23e69bb6583 o__Clostridiales f__Lachnospiraceae
## 1dda53416f231a3345668df39d4ae780 o__Clostridiales f__Lachnospiraceae
## ebf61ede654f305596908efa9a8ddf54 o__Erysipelotrichales f__Erysipelotrichaceae
## d2a695aa271adf072749a729fd96a731 o__Clostridiales f__Ruminococcaceae
## ebe91a7d912f432d8726b0cef99d18c3 o__Clostridiales f__Lachnospiraceae
## Genus Species
## e00f85cb3452e37943500e86afb268f4 g__Blautia <NA>
## f966e124604e0e32b209b88df6e42cd4 g__Faecalibacterium <NA>
## 42de4e368cca3ff50954f82c12dd5315 g__Ruminococcus <NA>
## bb1b75f41ff9c9db1d1de41e8388eb52 g__Bacteroides <NA>
## 098c3bbd8234f4ac198297ac0bde957d g__Prevotella <NA>
## 5c4ca852b40641b3eb0ad23e69bb6583 g__Roseburia <NA>
## 1dda53416f231a3345668df39d4ae780 g__Coprococcus <NA>
## ebf61ede654f305596908efa9a8ddf54 g__ <NA>
## d2a695aa271adf072749a729fd96a731 g__Oscillospira <NA>
## ebe91a7d912f432d8726b0cef99d18c3 g__ <NA>Save
write.csv(prevalence_tert1, "../../Outputs/tables/Microbiome/Prevalence_Genus_Tertile1.csv")
write.csv(prevalence_tert2, "../../Outputs/tables/Microbiome/Prevalence_Genus_Tertile2.csv")
write.csv(prevalence_tert3, "../../Outputs/tables/Microbiome/Prevalence_Genus_Tertile3.csv")